Linking learning behavior analytics and learning science concepts: Designing a learning analytics dashboard for feedback to support learning regulation
Open accessAbstract
Abstract Technological advancements have generated a strong interest in exploring learner behavior data through learning analytics to provide both learner and instructor with process-oriented feedback in the form of dashboards. However, little is known about the typology of dashboard feedback relevant for different learning goals, learners and teachers. While most dashboards and the feedback that they give are based only on learner performance indicators, research shows that effective feedback needs also to be grounded in the regulatory mechanisms underlying learning processes and an awareness of the learner's learning goals. The design artefact presented in this article uses a conceptual model that visualizes the relationships between dashboard design and the learning sciences to provide cognitive and behavioral process-oriented feedback to learners and teachers to support regulation of learning. A practical case example is given that demonstrates how the ideas presented in the paper can be deployed in the context of a learning dashboard. The case example uses several analytics/visualization techniques based on empirical evidence from earlier research that successfully tested these techniques in various learning contexts.
AI snapshot
Written from the paper’s full text.
TL;DR
The paper proposes a conceptual model that links learning analytics dashboard (LAD) feedback to learning-science concepts—regulation of learning, feedback typology, goal orientation, and effectiveness/efficiency—to provide process-oriented cognitive and behavioral feedback to learners and teachers. It illustrates the model with dashboard case examples using visualization and process analytics, but does not empirically evaluate the model.
Key findings
- Most existing LADs rely on performance/outcome indicators and lack theoretical grounding in the learning sciences; the paper argues effective feedback must account for regulatory mechanisms, learning goals, and goal orientations (mastery, performance, approach/avoidance).
- The proposed model distinguishes cognitive feedback (corrective, epistemic, suggestive) from behavioral feedback, and links them to self-regulated and co-regulated learning phases: planning, monitoring, and adaptation.
- It recommends using task-specific subgoals (TSSGs), backward instructional design, resource/time utility, and effectiveness/efficiency indicators to determine feedback type and timing, and to complement statistical/data-mining approaches with process analytics.
- Case examples illustrate the model: a planning example requires 60% performance for topic C, four web lectures, two homework assignments, and expected time ≤5 hours; a monitoring example shows trial 5 result 80% vs required 90% (failed) with hints; an adaptation example reports result = 100%, 5 trials, 8 corrections, score = 10, ineffective time utility (3.5 h, 5.2 h, 0.5 h); a group example notes communication score 5/20 and initiative/attendance rated on average 10/20. These are illustrative, not empirical findings.
Methods
- Design Science in Information Systems research, using conceptual modeling; the paper focuses on the first two design-science principles: designing a purposeful artifact and establishing relevance.
- No new empirical sample or outcome study is reported; the conceptual model is derived from a review of LAD studies, feedback research, self-regulated/co-regulated learning, goal orientation, and effectiveness/efficiency concepts.
- Case examples use analytics/visualization techniques based on earlier empirical research; one group-interactivity example was piloted by analysis of data from the authors’ experiment at LeaForum using WeSpot activity logs.
Limitations
- Stated: implementation mechanisms (e.g., prototype design), evaluation, and expansion of the design are left as further research; the case examples are not exhaustive, do not provide technical implementation detail, and do not test which visualization techniques work better.
- Stated: detailed mechanisms for user intervention in feedback remain challenging.
- Evident: the conceptual model is not empirically validated in this paper; there is no participant sample, outcome measure, or formal evaluation of the proposed dashboard feedback.
Model-written summary. Check it against the paper before relying on it.
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Claims the paper makes
0 of 58 cited sources checked · 0 supported · 0 flagged
- Premisecitation
Common to all feedback LADs presented in the literature on dashboards is the lack of theoretical support grounded in the learning sciences and research on feedback and underlying mechanisms of learning processes.
“Common to all feedback LADs presented in the literature on dashboards is the lack of theoretical support grounded in the learning sciences (Sedrakyan, Järvelä & Kirschner, 2016) and research on feedback and underlying mechanisms of learning processes.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Sedrakyan, Järvelä & Kirschner, 2016
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- Premisecitation
The sooner students receive feedback on what they have done, the more effective it is for their learning.
“Research has shown that the sooner students receive feedback on what they have done, the more effective it is for their learning (Irons, 2008).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Irons, 2008
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- Premisecitation
Another issue with current tools is finding evidence for their formal validation, such as whether the tools fulfill their intended purpose, including having a positive impact on learning, encouraging more efficient learning, or more effective learning.
“Another issue with current tools is finding evidence for their formal validation, e.g.whether the tools fulfill their intended purpose, such as having a positive impact on learning; encouraging more efficient learning; or more effective learning (Ferguson et al., 2016), which suggests that LADs should consider built-in mechanisms to allow tracking effects from such interventions.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Ferguson et al., 2016
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- Premisecitation
Goal orientation has been found to affect learning behavior.
“Goal orientation has been found to affect learning behavior (Stevens & Gist, 1997).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Stevens & Gist, 1997
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- Premisecitation
In terms of intended goals, most dashboard studies limit themselves to student performance outcomes through self-reflection, awareness, and self-assessment, positioning learners in comparison with teacher specified and/or peer performance.
“In terms of the intended goals of dashboards, most studies limit themselves to student performance outcomes through self-reflection, awareness, and self-assessment (Bodily & Verbert, 2017) positioning learners in comparison with teacher specified and/or peer performance.”
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- Premisecitation
Successful learners use a repertoire of strategies to guide and enhance their learning process cognitive, behavioral and motivational toward completing academic tasks.
“Research has shown that successful learners use a repertoire of strategies to guide and enhance their learning process cognitive, behavioral and motivationaltoward completing academic tasks (Zimmerman & Schunk, 2011).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Zimmerman & Schunk, 2011
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- Premisecitation
Most current Learning Analytics Dashboards (LADs) are based only on learner performance indicators.
“For example, most current Learning Analytics Dashboards (LADs) are based only on learner performance indicators (e.g., where a learner is doing well/poor, how much content has been completed, how much time was spent, how learners' progress compares to teacher specified and/or peer scores) that do not seem to contribute to learners' motivation and engagement (Blumenfeld, 1992;Elliott & Harackiewicz”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Blumenfeld, 1992 source
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- cites Elliott & Harackiewicz, 1996
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- Premisecitation
Learner performance indicators do not seem to contribute to learners' motivation and engagement.
“For example, most current Learning Analytics Dashboards (LADs) are based only on learner performance indicators (e.g., where a learner is doing well/poor, how much content has been completed, how much time was spent, how learners' progress compares to teacher specified and/or peer scores) that do not seem to contribute to learners' motivation and engagement (Blumenfeld, 1992;Elliott & Harackiewicz”
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- cites Elliott & Harackiewicz, 1996
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- Premisecitation
When using performance-oriented dashboards, learner mastery orientation decreases.
“Furthermore, recent research on the effectiveness of learning analytics tools reveals that when using performance-oriented dashboards, learner mastery orientation decreases (Lonn, Aguilar, & Teasley, 2015).”
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- Premisecitation
In the context of LADs feedback, data mining approaches prevail targeting performance visualization and outcome feedback rather than process-oriented feedback.
“In terms of analysis approach in the context of LADs feedback, data mining approaches prevail targeting performance visualization and outcome feedback (”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Sedrakyan, De Weerdt, & Snoeck, 2016
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- Interpretationreasoning
Tracking regulation patterns during a learning process can be helpful in determining possible intervention needs during a learning process.
“In summary, tracking regulation patterns during a learning process can be helpful in determining possible intervention needs during a learning process (e.g.,”
Quote in the paperSubstantial claim - Premisecitation
Little is known about the typology of dashboard feedback relevant for different learning goals, learners, and teachers.
“However, little is known about the typology of dashboard feedback relevant for different learning goals, learners and teachers.”
- Also stated in: § 1. Introduction · plain
“Despite their popularity and the proliferation of solution providers, little is known about design aspects, such as the typology of feedback relevant in a learning context (Sedrakyan, Järvelä & Kirschner, 2016;Yigitbasioglu & Velcu, 2012).”
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- cites Yigitbasioglu & Velcu, 2012
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- Also stated in: § 1. Introduction · plain
- Premisecitation
Several LADs target at delivering cognitive feedback in a limited context, such as mathematical problems or formal assessment of writing drafts.
“Several LADs target at delivering cognitive feedback in a limited context (e.g., mathematical problems or formal assessment of writing drafts (Ferguson et al., 2016)).”
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- Premisecitation
Most visual representations in the context of LADs are limited to graphs, charts, or other diagrams without providing support mechanisms to facilitate their interpretation.
“Furthermore, most visual representations in the context of LADs are limited to graphs, charts, or other diagrams without providing support mechanisms to facilitate their interpretation (Park & Jo, 2015).”
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- Premisecitation
Acceptance of a dashboard tool by end-users is a factor that can interfere with the achievement of its intended goal and thus affect learner performance.
“Also, as dashboards are tools to be used by the teacher and/or the learner, acceptance of this tool by end-users is yet another factor that can interfere with the achievement of their intended goal (Davis, 1989) and thus affect learner performance.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Davis, 1989 source
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- Premisecitation
In terms of data collection, most dashboard studies are limited to logs that address university settings.
“In terms of data collection, most studies are limited to logs that address university settings (Schwendimann et al., 2016).”
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- Premisecitation
When students have clear objectives, they are more likely to seek feedback to close the gap between their current understanding or skills and the desired goal.
“Moreover, when students have clear objectives, they are more likely to seek feedback to close the gap between their current understanding or skills and the desired goal (Hattie & Timperly, 2007).”
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- Premisecitation
The self-goal setting process can improve students' learning and motivation.
“The self-goal setting process can improve students' learning and motivation (Zimmerman, 1990).”
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- Premisecitation
Giving people specific goals to achieve rather than telling them to do their best increases their motivation.
“It has long been known that giving people specific goals to achieve rather than telling them to do their best increases their motivation (Locke & Latham, 2002).”
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- Interpretationnone given
Knowing learning goals and how much regulation effort the learner has put into goal achievement is not enough to determine the potential time when feedback would be most relevant to an end user.
“Knowing learning goals and how much (regulation) effort the learner has put into goal achievement is not enough to determine the potential time when feedback would be most relevant to an end user.”
Quote in the paperSubstantial claim - Interpretationreasoning
The actual use of learning resources can to a certain extent be indicative of learning outcomes.
“This means that the actual use of learning resources can to a certain extent be indicative of learning outcomes.”
Quote in the paperSubstantial claim - Premisecitation
Setting goals increases achievement.
“Second, setting goals increases achievement (Latham & Locke, 2007).”
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- Premisecitation
A process-analytics based feedback approach has been empirically tested in a learning context.
“This type of profile exploits a process-analytics based feedback approach that has been empirically tested in a learning context (Sedrakyan, Snoeck & De Weerdt, 2014;Sedrakyan, De Weerdt & Snoeck, 2016).”
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- cites Sedrakyan, De Weerdt & Snoeck, 2016
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- Interpretationreasoning
The combination of a learner's resource and time utility and self- or co-regulation effort can provide information about intervention needs.
“The combination of a learner's resource and time utility and (self-, co-) regulation effort, e.g., another attempt to improve his or her solution based on cognitive feedback, can provide information about intervention needs.”
Quote in the paperSubstantial claim - Premisecitation
When instructors set explicit learning goals, students have a clear picture of course expectations, helping them to concentrate their efforts efficiently toward the attainment of those goals.
“When instructors set explicit learning goals, students have a clear picture of course expectations, helping them to concentrate their efforts efficiently toward the attainment of those goals (Turkay, 2014).”
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- Premisecitation
Cognitive suggestive or corrective feedback for improving outcomes has been empirically tested in a learning context.
“The profile (see the discussed theoretical concept in section 3.2.1,3.2.2 and the measurment and presentation layer concepts in sections 4.1 and 4.3) also includes cognitive suggestive or corrective feedback for improving (for two failed questions in this case) the effectiveness of which has been empirically tested in a learning context (Sedrakyan,, & Snoeck, 2012).”
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- Interpretationreasoning
Tracking the utility of learning material can inform a learner or teacher if a learner effectively makes use of a learning resource defined for a specific goal.
“Tracking the utility of the learning material can inform a learner/teacher if a learner effectively makes use of a learning resource defined for a specific goal.”
Quote in the paperSubstantial claim - Premisecitation
The effectiveness of area- and bar-charts visualization techniques in quantifying achievement levels and supporting progress awareness has been shown in different learning contexts.
“The effectiveness of these techniques in quantifying achievement levels and supporting progress awareness has been shown in different learning contexts (Charleer, Klerkx, Duval, Laet, & Verbert, 2017).”
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- Premisecitation
Earlier research has shown that increasing effort (repetitive attempts) that does not lead to improved outcomes may suggest that students experience difficulties.
“For example, earlier research has shown that increasing effort (repetitive attempts) that does not lead to improved outcomes may suggest that students experience difficulties (Sedrakyan, 2016).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Sedrakyan, 2016
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- Interpretationreasoning
TSSGs are visible markers of learners' self-regulated learning at different points in time.
“To that extent, they are visible markers of learners SRL at different points in time.”
Quote in the paperSubstantial claim - Premisecitation
Social influence can play an important role in students' motivations.
“In this work, we motivate the use of an aggregated view approach by the fact that social influence can play an important role in students' motivations (Gruzd, Staves, & Wilk, 2012).”
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- Premisecitation
The use of process mining has been successfully tested in various learning contexts for exploring interactivity and learning regulation.
“Furthermore, the use of process mining has been successfully tested in various learning contexts for exploring the interactivity and learning regulation (Sedrakyan, 2016;Sedrakyan et al., 2014;Sedrakyan et al., 2016;Schoor & Bannert, 2012).”
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- cites Sedrakyan et al., 2014
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- cites Sedrakyan et al., 2016
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- Interpretationreasoning
Based on a conceptual analysis, a conceptual model that can be used to design information systems as a basis for process-oriented feedback in the context of LADs was derived.
“Based on a conceptual analysis, a conceptual model that can be used to design information systems as a basis for processoriented feedback in the context of LADs was derived.”
Quote in the paperSubstantial claim - Interpretationreasoning
The visualizations of activities (sequences and frequencies) can suggest to a teacher if a satisfactory level of group dynamics has been reached.
“The visualizations of activities (sequences and frequencies) can suggest to a teacher if a satisfactory level of group dynamics has been reached.”
Quote in the paperSubstantial claim- Rests on an assumption: Group dynamics and interactivity are to a large extent predictive for the collaborative nature of learning processes.
- Interpretationreasoning
Cognitive feedback can support learning regulation at a level of task-specific goals aiming to improve intermediate learning outcomes.
“Cognitive feedback can support learning regulation at a level of task-specific goals aiming to improve intermediate learning outcomes, whereas behavioral feedback should provide information about needs for behavioral change by improving awareness of learning progress.”
Quote in the paperNot a substantial claim - Premisecitation
From a learning sciences perspective, the learning process can be positively influenced by dashboard feedback if it takes into consideration the regulatory mechanisms underlying learning processes.
“In summary, from a learning sciences perspective, the learning process can be positively influenced by dashboard feedback if it takes into consideration the regulatory mechanisms underlying learning processes (Zimmerman, 1990).”
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- Interpretationreasoning
Process/sequence analytics techniques can help reveal issues not visible by quantification of interactions only.
“In this specific example, although the use of statistics can reveal a participation to group work, process/sequence analytics techniques can help revealing issues not visible by quantification of interactions only.”
Quote in the paperSubstantial claim- Rests on an assumption: Group dynamics and interactivity are to a large extent predictive for the collaborative nature of learning processes.
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- May combine two claims. 16
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FindingreasoningGroup 6 shows a desired (e.g., balanced) interaction according to the goals.
“According to the goals, Group 6 shows a desired (e.g., balanced) interaction.”
Quote in the paperNot a substantial claim- Rests on an assumption: Group dynamics and interactivity are to a large extent predictive for the collaborative nature of learning processes.
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FindingreasoningIn groups 1-5, there are some problems with collaboration because either not all participants are involved or some participants' activities are limited.
“For instance, if a teacher specified a balanced interactivity for a task in which a group member should contribute to active discussion on the analysis results, it is obvious from Figure 8 that in groups 1-5, there are some problems with collaboration because either not all participants are involved or some participants' activities are limited.”
Quote in the paperNot a substantial claim- Rests on an assumption: Group dynamics and interactivity are to a large extent predictive for the collaborative nature of learning processes.
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PremisecitationIn the context of feedback research, cognitive and behavioral approaches translate into two major forms: explanations targeted at improving cognitive dimensions of knowledge and guidance to influence a learner's behavior.
“In the context of feedback research, these approaches translate into two major forms: explanations targeted at improving cognitive dimensions of knowledge (e.g., understanding) and guidance to influence a learner's behavior (e.g., engaging in a specific type of activity believed to be related to a successful learning path; Sedrakyan, 2016).”
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PremisecitationMost research on educational dashboards lacks both theoretical support from recent advancements in the learning sciences and an evidence-informed foundation for choosing data to observe and assess learning processes.
“According to Sawyer (2014), most research on educational dashboards lacks both theoretical support from recent advancements in the learning sciences and an evidenceinformed foundation for choosing the data that can assist in observing and assessing learning processes to identify the feedback needs of learners/teachers.”
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PremisecitationFeedback mechanisms that the environment or peers provide can also be considered as a form of co-regulated learning.
“Although self-regulation concerns individual adaptation, feedback mechanisms that the environment or peers provide can also be considered as a form of co-regulated learning (Isohätälä, Järvenoja, & Järvelä, 2017).”
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PremisecitationCognitive feedback aims to improve learners' understanding of intermediate solutions allowing them to engage in self-regulatory learning mechanisms.
“This type of feedback aims to improve learners' understanding of intermediate solutions allowing them to engage in self-regulatory learning mechanisms (van Merriënboer & Kirschner, 2012).”
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PremisecitationLearner goal orientations and differences in achievement levels need to be carefully considered in the design of any intervention, as the resulting approaches and tools can affect students' interpretations of their data and subsequent academic success.
“Furthermore, recent studies on the effectiveness of existing dashboards suggest that learner goal orientations (Lonn et al., 2015) and differences in achievement levels (Park & Jo, 2015) need to be carefully considered in the design of any intervention, as the resulting approaches and tools can affect students' interpretations of their data and subsequent academic success.”
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- cites Park & Jo, 2015
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PremisecitationSelf-regulated and strategic learning involves experimenting with, and learning about, effective strategies for regulating aspects of their own, peers', and groups' shared learning processes, including planning, setting goals, organizing, monitoring, and adapting.
“In practice, self-regulated and strategic learning involves experimenting with, and learning about, effective strategies for regulating aspects of their own, peers', and groups' shared learning processes (Winne, Hadwin, & Perry, 2013), including planning, setting goals, organizing, monitoring, and adapting.”
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PremisecitationLearning may be explained by a combination of two basic approaches: cognitive theories and behavioral theories.
“Even though psychologists and educators are not in complete agreement, most agree that learning may be explained by a combination of two basic approaches: cognitive theories (i.e., cognitivism, which views the learning process as a step-by-step knowledge construction process) and behavioral theories (i.e., behaviorism, in which learning is defined as a change of the behavior of a learner by rein”
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PremisecitationCo-regulated learning occurs when learners' regulatory activities are guided, supported, shaped, or constrained by others, such as peers or teachers, and the social system, including the learning environment.
“Co-regulated learning (CoRL) occurs when learners' regulatory activities are guided, supported, shaped, or constrained by others, such as peers or teachers, and the social system, including the learning environment (Hadwin, Järvelä & Miller, 2017).”
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Interpretationnone givenThe design artefact presented in the article uses a conceptual model that visualizes the relationships between dashboard design and the learning sciences to provide cognitive and behavioral process-oriented feedback to learners and teachers to support regulation of learning.
“The design artefact presented in this article uses a conceptual model that visualizes the relationships between dashboard design and the learning sciences to provide cognitive and behavioral process-oriented feedback to learners and teachers to support regulation of learning.”
Quote in the paperSubstantial claim- Rests on an assumption: The conceptual model presented is a valid guide for designing LAD feedback to support regulation of learning.
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PremisecitationRegulation of learning and performance is central to research on feedback.
“The regulation of learning and performance is central to research on feedback (Butler & Winne, 1995).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Butler & Winne, 1995 source
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PremisecitationBehavioral change and improved performance were observed when supporting learners in the interpretation of visualizations.
“Empirical studies show that behavioral change and improved performance were observed when supporting learners in the interpretation of visualizations (Sedrakyan, 2016).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Sedrakyan, 2016
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InterpretationcitationIn terms of data analysis approaches, process (sequence) analytics should be considered as opposed to the statistical and data-mining approaches currently widely applied in research on learning analytics.
“This type of process can be referred to as a sequencing of cognitive and behavioral activities, suggesting that in terms of data analysis approaches, we also need to consider the role of process (sequence) analytics (Sedrakyan, 2016;Sedrakyan, Snoeck & De Weerdt, 2014) as opposed to the statistical and data-mining approaches currently widely applied in research on learning analytics.”
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- cites Sedrakyan, Snoeck & De Weerdt, 2014
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PremisecitationThe process-analytics approach allowed exploring learning process patterns that associate with better or worse learning outcomes, subsequently being used as guidelines to improve teaching.
“The approach allowed exploring learning process patterns that associate themselves with better/worse learning outcomes, subsequently being used as guidelines to improve teaching (Sedrakyan, De Weerdt & Snoeck, 2016;Sedrakyan, 2016).”
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- cites Sedrakyan, 2016
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InterpretationreasoningAdaptation profiles can use learner behavior following viewing and monitoring activities to show approximations of adaptation effort.
“These profiles can use learner behavior following viewing and monitoring activities to show approximations of adaptation effort.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationDepending on the types of goals learners possess, learning outcomes will target different levels of knowledge, skills, competences, or simply task completion, which also determines how learners engage with the regulated learning process.
“Depending on the types of goals learners possess, learning outcomes will target different levels of knowledge, skills, competences, or simply task completion, which also determines how learners engage with the regulated learning process (Winne, Hadwin, & Perry, 2013).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Winne, Hadwin, & Perry, 2013
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PremisecitationA planning profile includes activities such as planning the sub-goals to reach, selection of learning strategy (action plan), the materials and resources to use, and allocation of time.
“In general, a planning profile includes activities such as a) planning the sub-goals to reach, b) selection of learning strategy (action plan), c) the materials and resources to use, and c) allocation of time (Pintrich, 2000).”
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Interpretationnone givenA co-adaptation profile will utilize the concept of peer-oriented feedback both for individual and group learning plans.
“A co-adaptation profile will utilize the concept of peer-oriented feedback (e.g., “Could you give feedback on your peer task results?”) both for individual and group learning plans.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
InterpretationreasoningA self-monitoring profile can inform a learner about his or her progress with respect to an action plan and self-defined goals.
“A self-monitoring profile can inform a learner about his or her progress with respect to an action plan and self-defined goals.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
Interpretationnone givenPlanning profiles will be used both to guide learners and inform teachers about the coherence and alignment between learners' and teacher's specified goals and action plans.
“These planning profiles will be used both to guide learners and inform teachers about the coherence and alignment between learners' and teacher's specified goals and action plans.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
InterpretationreasoningA learner's attempt in combination with outcomes, utilized time and resources will allow measuring if the self-regulation effort was successful or sufficient.
“whereas a learner's attempt in combination with outcomes, utilized time and resources (as well as feedback received on earlier attempt) will allow measuring if the self-regulation effort was successful or sufficient.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningAbsence of another trial might suggest a need for behavioral feedback to engage a learner in a regulatory process.
“Absence of another trial might for instance suggest a need for behavioral feedback to engage a learner in a regulatory process,”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
InterpretationreasoningBy creating visible artefacts of varying sub-goals related to mastering new knowledge or skills, we can make them observable in the learning process for both instructor and learner.
“That is, by creating visible artefacts of varying sub-goals related to mastering the new knowledge or skills and/or competence, we can make them observable in the learning process for both the instructor and learner.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationTask-specific subgoals (TSSG) include steps that are specific, measurable, action-oriented, realistic, and temporal.
“Task-specific subgoals (TSSG) include steps that are a) specific, b) measurable, c) action-oriented, d) realistic, and e) temporal (Winne & Hadwin, 1998).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Winne & Hadwin, 1998
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThe Technology Acceptance Model suggests ease of use and perceived usefulness as important factors contributing to the user acceptance (i.e., actual utility) of LAD feedback.
“Previous studies have identified important variables dealing with user acceptance for computer-assisted learning environments, such as the Technology Acceptance Model (Davis, 1989), which suggests ease of use and perceived usefulness as important factors contributing to the user acceptance (i.e., actual utility) of LAD feedback.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Davis, 1989 source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationBackward design uses goal setting as the focal point of lesson design.
“For example, “backward design,” which is a well-known instructional design model, uses goal setting as the focal point of lesson design (Wiggins & McTighe, 1998).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Wiggins & McTighe, 1998
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenPlanning profiles can provide additional information about the overall level of preparedness, preferences and difficulties of learners.
“The planning profiles can also provide additional information about the overall level of preparedness, preferences and difficulties of learners.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
InterpretationreasoningAn increased or decreased effort in combination with achievements can be indicative of whether activities had an impact on achievement.
“For instance, an increased or decreased effort in combination with achievements (e.g., score) can be indicative of whether activities, such as attending classes, use of a learning resource, task completion, or received feedback had an impact on achievement.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningBased on learners' goals, LADs should be able to propose personalized learning trajectories (action plans), also taking into consideration the level of preparedness and dependencies between learning (sub-)goals.
“We posit that based on these goals, LADs should be able to propose personalized learning trajectories (action plans), also taking into consideration the level of preparedness and dependencies between learning (sub-)goals.”
Quote in the paperNot a substantial claim- Rests on an assumption: The learning science concepts reviewed (regulation of learning, feedback typology, goal orientation, efficiency and effectiveness) are the relevant and sufficient concepts for designing LAD feedback.
Classified as probably not a substantial claim.
InterpretationreasoningThe conceptual model can serve as a general framework for designing and building information systems as a basis for educational dashboard feedback.
“This model can serve as a general framework for designing and building information systems as a basis for educational dashboard feedback.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
InterpretationreasoningDetection of inefficient processes can be useful in determining the timeliness (receive time) of the feedback.
“we posit that the detection of inefficient processes can be useful in determining the timeliness (receive time) of the feedback.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
InterpretationreasoningAdaptation profiles can inform learners about the level of effort put into learning regulation and the needs for adaptation.
“Adaptation profiles can inform learners about the level of effort put into learning regulation and the needs for adaptation (e.g., how learners perform with learning challenges, the resources they use, how much time they spend, and whether they need additional feedback).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationGoal-directed behavior that results from self-goal setting is empowering and proactive, as students take responsibility and ownership.
“Goal-directed behavior that results from self-goal setting is empowering and proactive, as students take responsibility and ownership (Elliot & Fryer, 2008).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Elliot & Fryer, 2008
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationWhen using backward design, instructors identify learning goals first, determine acceptable evidence, and plan learning experiences and instruction to achieve those goals.
“When using backward design, instructors identify learning goals for the course first, considering what they want students to know and be able to do when they finish the course, and then determine acceptable evidence regarding whether those goals are met and plan learning experiences and instruction to achieve those learning goals (Wiggins & McTighe, 1998).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Wiggins & McTighe, 1998
Not checked in the free run.
Probably not a substantial claim: may not pass S4 (based). May combine two claims.
InterpretationreasoningA self-adaptation profile will inform a learner about how successful he or she was in addressing a challenge and what actions he or she can take to address a detected challenge.
“A self-adaptation profile will inform a learner about how successful he or she was in addressing a challenge and what actions he or she can take to address a detected challenge.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationcitationBy complementing the idea of feedback with the concepts of effectiveness and efficiency, it is possible to assess learning progress with respect to potential intervention needs and to determine optimal receive time of feedback by discovering inefficient or ineffective processes during learning.
“In summary, by complementing the idea of feedback with the concepts of effectiveness and efficiency, it is possible to assess a learning progress with respect to potential intervention needs and, more specifically, with respect to determining optimal”
Quote in the paperNot a substantial claim- Rests on an assumption: The learning science concepts reviewed (regulation of learning, feedback typology, goal orientation, efficiency and effectiveness) are the relevant and sufficient concepts for designing LAD feedback.
May combine two claims.
InterpretationreasoningOutputs of TSSGs and engagement patterns will allow distinguishing the needs for the type of feedback (cognitive or behavioral) and expanding dashboard performance visualizations with textual feedback to learners and teachers.
“In summary, while the outputs of TSSGs and engagement patterns will allow distinguishing the needs for the type of feedback (cognitive or behavioral), these also allow expanding dashboard performance visualizations with textual feedback to learners and teachers.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
InterpretationreasoningThe model allows for the generation of process-oriented feedback to different users related to different learning goals.
“The model allows for the generation of process-oriented feedback to different users (e.g., teacher and learners) related to different learning goals.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Interpretationnone givenIn the planning phase using backward design, the task-specific sub-goals necessary to obtain a knowledge or skill are linked with mastering the proposed learning material.
“In the planning phase, using backward design, the task specific sub-goals necessary to obtain a knowledge or skill are linked with mastering the proposed learning material.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
InterpretationreasoningIf the expected utility of a specific resource within an agreed time does not lead to achievement of the expected learning goal, it might suggest that the resource contains a certain level of difficulty for that cluster, and vice versa.
“For example, if the expected utility of a specific resource within a meaningful (agreed) time does not lead to the achievement of the expected learning goal, either for a specific learner or group of learners (depending on the observation target), it might suggest that the resource for a specific goal contains a certain level of “difficulty” for that cluster, and vice versa.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
PremisecitationAccording to goal-setting theory, goals should be short term rather than long term and challenging, and should be Specific, Measurable, Achievable, Relevant, and Time-framed (SMART).
“According to goal-setting theory, goals should be short term rather than long term and challenging (Latham & Locke, 2002) and “Specific, Measurable, Achievable, Relevant, Time-framed” (SMART; Drucker, 1954).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Latham & Locke, 2002
Not checked in the free run.
- cites Drucker, 1954
Not checked in the free run.
Probably not a substantial claim: may not pass S4 (based). May combine two claims.
InterpretationreasoningThe role of the monitoring profile in the context of dashboards is to detect and suggest further adaptation needs for learners and inform teachers about difficulties if the expected performance level is not being achieved within an agreed action plan and timeframe.
“In general, the role of the monitoring profile in the context of dashboards is to detect and suggest further adaptation needs for learners and inform teachers about difficulties if the expected performance level is not being achieved within an agreed action plan and timeframe (see the case example for further details).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningLearning resources can serve as artefacts of instructor expectations or planning.
“These resources can serve as artefacts of instructor expectations or planning.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
InterpretationreasoningA co-monitoring profile can provide information about how well a learner performs with respect to his or her peers if the goal orientation is set to performance.
“A comonitoring profile can also provide information about how well a learner performs with respect to his or her peers if the goal orientation is set to “performance”.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductown dataThe feedback idea presented in this example was piloted by the analysis of data from the authors' experiment conducted at LeaForum.
“The feedback idea presented in this example was piloted by the analysis of data from the authors' experiment conducted at LeaForum (http://www.oulu.fi/leaf-eng/).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
InterpretationreasoningGroup standards can be used to provide group-oriented monitoring feedback.
“Group standards can be used to provide group-oriented monitoring feedback (see the discussed theoretical concept in section 3.2.1,3.2.2 and the measurment and presentation layer concepts in sections 4.1 and 4.3).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based). May combine two claims.
InterpretationreasoningThe proposed design model allows extending dashboard feedback with textual explanations in the form of cognitive and behavioral feedback, which makes learning-process data visualizations easier to interpret for an end user.
“Ultimately, the proposed design model allows extending dashboard feedback with textual explanations in the form of cognitive and behavioral feedback which makes learning-process data visualizations easier to interpret for an end user.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe case example makes use of visualization techniques based on empirical evidence from earlier research.
“as the case example makes use of visualization techniques based on empirical evidence from earlier research, investigating other visualization techniques and testing which techniques can work better in supporting the feedback ideas presented in this paper can yet be another possible future research direction.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationSeveral researchers argue that showing an aggregated analysis with respect to peer performance is a good approach.
“Several researchers argue that showing an aggregated analysis with respect to peer performance is a good approach (Lonn et al., 2015).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Lonn et al., 2015 source
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.
InterpretationreasoningSuch feedback can be constructed based on learner profiles of regulation process phases spanning the planning, monitoring, and adapting activities, thus allowing detecting inefficient processes in learning.
“Such feedback can be constructed based on learner profiles of regulation process phases spanning the planning, monitoring, and adapting activities thus allowing detecting inefficient processes in learning.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningBackward instructional design provides richer opportunities for tracking learning processes due to the use of goal setting as the focal point of lesson design and TSSGs, which combine steps that are measurable, action-oriented, and temporal.
“Further,”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenVisual inspection can easily reveal if the interactions are balanced within a group or concentrated among particular members.
“For instance, visual inspection can easily reveal if the interactions are balanced within a group or concentrated among particular members.”
Quote in the paperNot a substantial claim
The paper relies on this without support.
The conceptual model presented is a valid guide for designing LAD feedback to support regulation of learning.
“The design artefact presented in this article uses a conceptual model that visualizes the relationships between dashboard design and the learning sciences to provide cognitive and behavioral process-oriented feedback to learners and teachers to support regulation of learning.”
“The model aims to address the gap between LADs and learning theories serving as both a basis for building information systems and thus also helping to understand future data needs, and an initial platform to guide future research in this domain.”
The paper relies on this without support.
The learning science concepts reviewed (regulation of learning, feedback typology, goal orientation, efficiency and effectiveness) are the relevant and sufficient concepts for designing LAD feedback.
“This section provides a preliminary discussion on what concepts we need to support in the design of dashboard feedback to make it possible to observe learning processes with respect to potential feedback (i.e., regulation in learning) needs of different learners for different learning goals.”
“In this work, we complement the engineering approach with theories in the learning sciences to design a novel artefact, namely, a process-oriented feedback model in the context of LADs.”
The authors state this as an assumption.
TSSGs are initially carefully defined by an instructor in the context of instruction design and in accordance with goal-setting theory.
“To enable dashboard feedback based on TSSGs, we create the premise that they are initially carefully defined by an instructor in the context of instruction design and in accordance with goal-setting theory.”
The authors state this as an assumption.
A teacher is responsible for ensuring the quality of a goal in terms of being SMART.
“In this paper, we assume that a teacher is responsible for ensuring the quality of a goal in terms of being SMART.”
The paper relies on this without support.
Goals provide learners with standards against which they can monitor the learning process and progress.
“From a self-regulated learning perspective, goals provide learners with standards against which they can monitor the learning process and progress.”
The paper relies on this without support.
Metacognitive monitoring enables learners to adjust or change their goals, plans, or strategies for learning.
“From a regulated learning point of view, metacognitive monitoring enables learners to adjust or change their goals, plans, or strategies for learning.”
The paper relies on this without support.
User acceptance can be an important factor affecting the successful use of dashboard feedback.
“Despite the intended benefits of dashboards, user acceptance can be yet another important factor affecting the successful use of dashboard feedback.”
The paper relies on this without support.
Monitoring can prove distracting and thereby alter the nature of learning in a negative way.
“During the learning process, monitoring can prove distracting and thereby alter the nature of learning in a negative way.”
The paper relies on this without support.
Too much feedback can be as harmful as delayed feedback.
“Too much feedback can be as harmful as delayed feedback.”
The paper relies on this without support.
Group dynamics and interactivity are to a large extent predictive for the collaborative nature of learning processes.
“The motivation for this choice is that group dynamics and interactivity are to a large extent predictive for the collaborative nature of learning processes.”
The paper relies on this without support.
Existing LAD solutions mainly focus on performance orientation.
“which is the main focus of existing solutions.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Several studies have explored the potential for LA to improve feedback practices since the emergence of the field in 2011.
“Since the emergence of the field of LA in 2011, several studies have explored the potential for LA to improve feedback practices (Misiejuk et al., 2021; Pardo, 2018; Sedrakyan et al., 2020).”
From A systematic review of the role of learning analytics in enhancing feedback practices in higher education (2022) · cites this paper as Sedrakyan et al., 2020
Sedrakyan et al. (2020) and Lim et al.'s (2021) found that the use of LA-based feedback supported self-regulated learning.
“Sedrakyan et al. (2020) and Lim et al. ' s (2021) found that the use of LA-based feedback supported self-regulated learning”
From A systematic review of the role of learning analytics in enhancing feedback practices in higher education (2022) · cites this paper as Sedrakyan et al., 2020
LA feedback for students is mostly focused on their own learning performance to provide personalized feedback and to help them with regulating their learning.
“while LA feedback for students is mostly focused on their own learning performance to provide personalized feedback and to help them with regulating their learning (e.g., Pardo et al., 2019; Sedrakyan et al., 2020).”
From A systematic review of the role of learning analytics in enhancing feedback practices in higher education (2022) · cites this paper as Sedrakyan et al., 2020
Cites
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483 · see citing works
Hattie, J., & Timperley, H. (2007) · Review of Educational Research · doi:10.3102/003465430298487 · Cited by 12,905 · see citing works
(1999) · Cited by 12,812 · see citing works
Design Science in Information Systems Research1
Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004) · MIS Quarterly · doi:10.2307/25148625 · Cited by 10,707 · cited 2× · see citing works
“According to Hevner et al. (2004), two paradigms characterize much of the research in the Information Systems discipline: behavioral science and design science.”
BackgroundMethodA social-cognitive approach to motivation and personality.
Dweck, C. S., & Leggett, E. L. (1988) · Psychological Review · doi:10.1037/0033-295x.95.2.256 · Cited by 9,745 · see citing works
Design science in information systems research
(2004) · Journal of the Association for Information Systems · doi:10.5555/2017212.2017217 · Cited by 8,254 · see citing works
Building a practically useful theory of goal setting and task motivation: A 35-year odyssey.
Locke, E. A., & Latham, G. P. (2002) · American Psychologist · doi:10.1037/0003-066x.57.9.705 · Cited by 7,766 · cited 2× · see citing works
“According to goal-setting theory, goals should be short term rather than long term and challenging (Latham & Locke, 2002) and “Specific, Measurable, Achievable, Relevant, Time-framed” (SMART; Drucker, 1954).”
BackgroundResearch Design: Qualitative and Quantitative Approaches
Wilson, R. D., & Creswell, J. W. (1996) · Journal of Marketing Research · doi:10.2307/3152153 · Cited by 7,061 · see citing works
Formative assessment and the design of instructional systems
Sadler, D. R. (1989) · Instructional Science · doi:10.1007/bf00117714 · Cited by 4,285 · see citing works
A 2 × 2 achievement goal framework.
Elliot, A. J., & McGregor, H. A. (2001) · Journal of Personality and Social Psychology · doi:10.1037//0022-3514.80.3.501 · Cited by 3,953 · see citing works
Self-Regulated Learning and Academic Achievement: An Overview
Zimmerman, B. J. (1990) · Educational Psychologist · doi:10.1207/s15326985ep2501_2 · Cited by 3,687 · see citing works
Feedback and Self-Regulated Learning: A Theoretical Synthesis
Butler, D. L., & Winne, P. H. (1995) · Review of Educational Research · doi:10.3102/00346543065003245 · Cited by 3,334 · see citing works
Approach and avoidance motivation and achievement goals
Elliot, A. J. (1999) · Educational Psychologist · doi:10.1207/s15326985ep3403_3 · Cited by 3,103 · see citing works
Multiple goals, multiple pathways: The role of goal orientation in learning and achievement
Pintrich, P. R. (2000) · Journal of Educational Psychology · doi:10.1037/0022-0663.92.3.544 · Cited by 2,254 · see citing works
Handbook of Self-Regulation of Learning and Performance
Schunk, D. H. (2011) · Routledge eBooks · doi:10.4324/9780203839010 · Cited by 2,191 · see citing works
Handbook of Self-Regulation of Learning and Performance
(2017) · Routledge eBooks · doi:10.4324/9781315697048 · Cited by 1,917 · see citing works
Studying as Self-Regulated Learning
(1998) · Metacognition in Educational Theory and Practice · doi:10.4324/9781410602350-19 · Cited by 1,589 · see citing works
Handbook of motivation science
Shah, J. Y., & Gardner, W. L. (2008) · Cited by 1,404 · see citing works
The Cambridge Handbook of the Learning Sciences
(2014) · doi:10.1017/cbo9781139519526 · Cited by 907 · see citing works
Research Design: Qualitative and Quantitative Approaches
Magnan, S. S., & Creswell, J. W. (1997) · Modern Language Journal · doi:10.2307/328794 · Cited by 878 · see citing works
Gašević, D., Dawson, S., Rogers, T., & Gašević, D. (2015) · The Internet and Higher Education · doi:10.1016/j.iheduc.2015.10.002 · Cited by 726 · see citing works
New Frontiers: Regulating Learning in CSCL
Järvelä, S., & Hadwin, A. F. (2013) · Educational Psychologist · doi:10.1080/00461520.2012.748006 · Cited by 722 · see citing works
Self-citationThe International Handbook of Collaborative Learning
Hmelo‐Silver, C. E., Chinn, C. A., Chan, C. K. K., O’Donnell, A. M., & Ebooks Corporation (2013) · Routledge eBooks · doi:10.4324/9780203837290 · Cited by 713 · see citing works
Self-Regulation, Co-Regulation, and Shared Regulation in Collaborative Learning Environments
Hadwin, A. F., Järvelä, S., & Miller, M. (2017) · Routledge eBooks · doi:10.4324/9781315697048-6 · Cited by 683 · see citing works
Self-citationFrøkjær, E., Hertzum, M., & Hornbæk, K. (2000) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/332040.332455 · Cited by 654 · see citing works
Learning Analytics Dashboard Applications
Verbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J. L. (2013) · American Behavioral Scientist · doi:10.1177/0002764213479363 · Cited by 640 · see citing works
Self-citationNew Developments in and Directions for Goal-Setting Research
Latham, G. P., & Locke, E. A. (2007) · European Psychologist · doi:10.1027/1016-9040.12.4.290 · Cited by 596 · see citing works
Classroom learning and motivation: Clarifying and expanding goal theory
Blumenfeld, P. C. (1992) · Journal of Educational Psychology · doi:10.1037/0022-0663.84.3.272 · Cited by 587 · see citing works
Avoidance achievement motivation: A personal goals analysis.
Elliot, A. J., & Sheldon, K. M. (1997) · Journal of Personality and Social Psychology · doi:10.1037//0022-3514.73.1.171 · Cited by 475 · see citing works
Motivational Sources and Outcomes of Self-Regulated Learning and Performance
Zimmerman, B. J. (2015) · Routledge eBooks · doi:10.4324/9780203839010.ch4 · Cited by 467 · see citing works
Learning dashboards: an overview and future research opportunities
Verbert, K., Govaerts, S., Duval, E., Santos, J. L., Assche, F. V., Parra, G., & Klerkx, J. (2013) · Personal and Ubiquitous Computing · doi:10.1007/s00779-013-0751-2 · Cited by 456 · see citing works
Self-citationGruzd, A. A., Staves, K., & Wilk, A. (2012) · Computers in Human Behavior · doi:10.1016/j.chb.2012.07.004 · Cited by 405 · see citing works
Campbell, R. N., & Terry, G. R. (1954) · Industrial and Labor Relations Review · doi:10.2307/2518875 · Cited by 405 · see citing works
A review of dashboards in performance management: Implications for design and research
Yigitbasioglu, O. M., & Velcu, O. (2012) · International Journal of Accounting Information Systems · doi:10.1016/j.accinf.2011.08.002 · Cited by 398 · see citing works
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
(2001) · doi:10.1145/365024 · Cited by 354 · see citing works
Avoidance achievement motivation: A personal goals analysis.
Elliot, A. J., & Sheldon, K. M. (1997) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.73.1.171 · Cited by 337 · see citing works
Behavioral Research: A Conceptual Approach
Schneider, K. C., & Kerlinger, F. N. (1979) · Journal of Marketing Research · doi:10.2307/3150838 · Cited by 332 · see citing works
Design and Implementation of a Learning Analytics Toolkit for Teachers
Dyckhoff, A. L., Zielke, D., Bultmann, M., Chatti, M. A., & Schroeder, U. (2012) · Cited by 315 · see citing works
Bodily, R., & Verbert, K. (2017) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2017.2740172 · Cited by 306 · see citing works
Self-citationEnhancing Learning through Formative Assessment and Feedback
Irons, A. D. (2007) · Routledge eBooks · doi:10.4324/9780203934333 · Cited by 304 · see citing works
Stevens, C. K., & Gist, M. E. (1997) · Personnel Psychology · doi:10.1111/j.1744-6570.1997.tb01490.x · Cited by 300 · see citing works
Developing early warning systems to predict students’ online learning performance
Hu, Y., Lo, C., & Shih, S.-P. (2014) · Computers in Human Behavior · doi:10.1016/j.chb.2014.04.002 · Cited by 278 · see citing works
Societal implications of nanoscience and nanotechnology: Maximizing human benefit
Roco, M. C., & Bainbridge, W. S. (2005) · Journal of Nanoparticle Research · doi:10.1007/s11051-004-2336-5 · Cited by 268 · see citing works
Development of the Learning Analytics Dashboard to Support Students' Learning Performance
Jo, I.-H., & Park, Y. (2020) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.3217/jucs-021-01-0110 · Cited by 203 · see citing works
Isohätälä, J., Järvenoja, H., & Järvelä, S. (2016) · International Journal of Educational Research · doi:10.1016/j.ijer.2016.10.006 · Cited by 197 · see citing works
Self-citationPhielix, C., Prins, F. J., Kirschner, P. A., Erkens, G., & Jaspers, J. (2010) · Computers in Human Behavior · doi:10.1016/j.chb.2010.06.024 · Cited by 190 · see citing works
Self-citationResearch Evidence on the Use of Learning Analytics: Implications for Education Policy
Ferguson, R., Andrew Brasher, A., Clow, D., Adam, C., Hillaire, G., Mittelmeier, J., Rienties, B., Ullmann, T., & Riina, V. (2016) · Research Explorer (The University of Manchester) · doi:10.2791/955210 · Cited by 179 · see citing works
Awareness of group performance in a CSCL-environment: Effects of peer feedback and reflection
Phielix, C., Prins, F. J., & Kirschner, P. A. (2009) · Computers in Human Behavior · doi:10.1016/j.chb.2009.10.011 · Cited by 167 · see citing works
Self-citationLonn, S., Aguilar, S. J., & Teasley, S. D. (2015) · Computers in Human Behavior · doi:10.1016/j.chb.2014.07.013 · Cited by 152 · see citing works
The influence of mastery‐avoidance goals on performance improvement
Yperen, N. W. V., Elliot, A. J., & Anseel, F. (2009) · European Journal of Social Psychology · doi:10.1002/ejsp.590 · Cited by 152 · see citing works
Constructive Technology Assessment and Socio-Technical Scenarios
Rip, A., & Kulve, H. T. (2008) · Springer eBooks · doi:10.1007/978-1-4020-8416-4_4 · Cited by 140 · see citing works
Bernacki, M. L., Byrnes, J. P., & Cromley, J. G. (2011) · Contemporary Educational Psychology · doi:10.1016/j.cedpsych.2011.12.001 · Cited by 125 · see citing works
Valdivia, I. M. Á., Espasa, A., & Guasch, T. (2011) · Studies in Higher Education · doi:10.1080/03075079.2010.510182 · Cited by 124 · see citing works
Supporting Self-Regulated Learning with gStudy Software: The Learning Kit Project **
Winne, P. H., Nesbit, J. C., Kumar, V., Hadwin, A. F., Lajoie, S. P., Azevedo, R., & Perry, N. E. (2006) · eScholarship@McGill (McGill) · Cited by 122 · see citing works
Schoor, C., & Bannert, M. (2012) · Computers in Human Behavior · doi:10.1016/j.chb.2012.02.016 · Cited by 116 · see citing works
Role of personality in computer based learning
Tlili, A., Essalmi, F., Jemni, M., Kinshuk, K., & Chen, N. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.07.043 · Cited by 98 · see citing works
Effects of feedback on collaborative writing in an online learning environment
Guasch, T., Espasa, A., Valdivia, I. M. Á., & Kirschner, P. A. (2013) · Distance Education · doi:10.1080/01587919.2013.835772 · Cited by 89 · see citing works
Self-citationTechnology Assessment for Responsible Innovation
Grünwald, A. (2014) · Springer eBooks · doi:10.1007/978-94-017-8956-1_2 · Cited by 81 · see citing works
Metacognition and Computer-Supported Collaborative Learning
Winne, P. H., Hadwin, A. F., & Perry, N. E. (2015) · Routledge eBooks · doi:10.4324/9780203837290.ch26 · Cited by 73 · see citing works
Assessing the effectiveness of feedback enabled simulation in teaching conceptual modeling
Sedrakyan, G., Snoeck, M., & Poelmans, S. (2014) · Computers & Education · doi:10.1016/j.compedu.2014.06.014 · Cited by 63 · see citing works
Self-citationProcess-mining enabled feedback: “tell me what I did wrong” vs. “tell me how to do it right”
Sedrakyan, G., De Weerdt, J., & Snoeck, M. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2015.12.040 · Cited by 62 · see citing works
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Rip, A., & Robinson, D. K. R. (2013) · Philosophy of engineering and technology · doi:10.1007/978-94-007-7844-3_3 · Cited by 53 · see citing works
Church, A. H., & Alford, L. P. (2020) · Routledge eBooks · doi:10.4324/9781003056584-9 · Cited by 51 · see citing works
weSPOT: A Personal and Social Approach to Inquiry-Based Learning
Mikroyannidis, A., Okada, A., Protopsaltis, A., Rusman, E., Chaimala, F., Kikis-Papadakis, K., Stefanov, K., Specht, M. M., Held, P., Boytchev, P., et al. (2020) · DSpace (Open University in the Netherlands) · doi:10.3217/jucs-019-14-2093 · Cited by 45 · see citing works
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Schwendimann, B. A., Rodríguez‐Triana, M. J., Vozniuk, A., Prieto, L. P., Boroujeni, M. S., Holzer, A., Gillet, D., & Dillenbourg, P. (2016) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2883851.2883930 · Cited by 45 · see citing works
Sedrakyan, G., Snoeck, M., & Weerdt, J. D. (2014) · Computers in Human Behavior · doi:10.1016/j.chb.2014.09.054 · Cited by 41 · see citing works
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Holbrook, J. (2014) · Journal of Responsible Innovation · doi:10.1080/23299460.2014.924240 · Cited by 40 · see citing works
van Merriënboer, J. J. G., Kirschner, P. A., & Frèrejean, J. (2024) · Routledge eBooks · doi:10.4324/9781003322481 · Cited by 33 · see citing works
Self-citationUse of Dashboards and Visualization Techniques to Support Teacher Decision Making
Mottus, A., Kinshuk, K., Sabine Graf, A., & Chen, N. (2014) · Lecture notes in educational technology · doi:10.1007/978-3-662-44659-1_10 · Cited by 29 · see citing works
Sedrakyan, G., Poelmans, S., & Snoeck, M. (2016) · Information and Software Technology · doi:10.1016/j.infsof.2016.11.001 · Cited by 24 · see citing works
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Sedrakyan, G., & Snoeck, M. (2012) · Lecture notes in business information processing · doi:10.1007/978-3-642-31072-0_30 · Cited by 22 · see citing works
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Charleer, S., Klerkx, J., Duval, E., & Verbert, K. (2017) · International Journal of Technology Enhanced Learning · doi:10.1504/ijtel.2017.10002503 · Cited by 22 · see citing works
Self-citationEvaluating Student-Facing Learning Dashboards of Affective States
Sedrakyan, G., Leony, D., Muñoz‐Merino, P. J., Kloos, C. D., & Verbert, K. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66610-5_17 · Cited by 22 · see citing works
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Tomic, W. (1993) · DSpace (Open University in the Netherlands) · Cited by 21 · see citing works
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Duval, E., Klerkx, J., Verbert, K., Nagel, T., Govaerts, S., Chico, G. A. P., Odriozola, J. L. S., & Vandeputte, B. (2012) · Lirias · Cited by 18 · see citing works
Self-citationSedrakyan, G., & Snoeck, M. (2017) · Communications in computer and information science · doi:10.1007/978-3-319-66302-9_4 · Cited by 18 · see citing works
Self-citationWESPOT: Inquiry based learning meets learning analytics
Specht, M. M., Bedek, M. A., Duval, E., Held, P., Okada, A., Stefanov, K., Parodi, E., Kikis-Papadakis, K., & Strahovnik (2012) · Open Research Online (The Open University) · Cited by 17 · see citing works
Sedrakyan, G. (2016) · Lirias · Cited by 17 · see citing works
Self-citationSedrakyan, G., Järvelä, S., & Kirschner, P. A. (2016) · Lirias · Cited by 14 · see citing works
Self-citationEffects of Simulation on Novices’ Understanding of the Concept of Inheritance in Conceptual Modeling
Sedrakyan, G., & Snoeck, M. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-25747-1_32 · Cited by 13 · see citing works
Self-citationTowards balanced discussions in the classroom using ambient information visualisations
Charleer, S., Klerkx, J., Duval, E., Laet, T. D., & Verbert, K. (2017) · International Journal of Technology Enhanced Learning · doi:10.1504/ijtel.2017.084501 · Cited by 13 · see citing works
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Hammoudi, S., Pires, L. F., Selić, B., & Desfray, P. (2017) · Communications in computer and information science · doi:10.1007/978-3-319-66302-9 · Cited by 10 · see citing works
Do we need to teach testing skills in courses on requirements engineering and modelling
Sedrakyan, G., & Snoeck, M. (2014) · Lirias · Cited by 8 · see citing works
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Rojas, J. A., Sedrakyan, G., Colpaert, P., Sande, M. V., & Verborgh, R. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-98192-5_28 · Cited by 8 · see citing works
Self-citationSedrakyan, G., & Snoeck, M. (2016) · International Conference on Model-Driven Engineering and Software Development · Cited by 5 · see citing works
Self-citationSedrakyan, G., Vocht, L. D., Alonso, J., Escalante, M., Orue-Echevarria, L., & Mannens, E. (2018) · International Conference on Model-Driven Engineering and Software Development · doi:10.5220/0006728206910698 · Cited by 5 · see citing works
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Carr‐Chellman, A. A. (2004) · Educational technology: The magazine for managers of change in education · Cited by 4 · see citing works
Leony, D., Sedrakyan, G., Muñoz‐Merino, P. J., Kloos, C. D., & Verbert, K. (2017) · Journal of Research in Innovative Teaching & Learning · Cited by 4 · see citing works
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Guasch, T., Espasa, A., Valdivia, I. M. Á., & Kirschner, P. A. (2011) · DSpace (Open University in the Netherlands) · Cited by 3 · see citing works
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Matcha, W., Uzir, N. A., Gašević, D., & Pardo, A. (2019) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2019.2916802 · Cited by 434 · see citing works
Process mining techniques and applications – A systematic mapping study
Garcia, C. D. S., Meincheim, A., Faria, E. R., Dallagassa, M. R., Sato, D. M. V., Carvalho, D. R., Santos, E. A. P., & Scalabrin, E. E. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.05.003 · Cited by 335 · see citing works
Learning analytics dashboard: a tool for providing actionable insights to learners
Sušnjak, T., Ramaswami, G., & Mathrani, A. (2022) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-021-00313-7 · Cited by 241 · see citing works
Banihashem, S. K., Noroozi, O., van Ginkel, S., Macfadyen, L. P., & Biemans, H. J. A. (2022) · Educational Research Review · doi:10.1016/j.edurev.2022.100489 · Cited by 246 · see citing works
Self-regulated learning and learning analytics in online learning environments
Viberg, O., Khalil, M., & Baars, M. (2020) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3375462.3375483 · Cited by 203 · see citing works
Learning analytics dashboards for adaptive support in face-to-face collaborative argumentation
Han, J., Kim, K. H., Rhee, W., & Cho, Y. H. (2020) · Computers & Education · doi:10.1016/j.compedu.2020.104041 · Cited by 141 · see citing works
Panigrahi, R., Srivastava, P. R., & Panigrahi, P. K. (2020) · Information Technology and People · doi:10.1108/itp-07-2019-0380 · Cited by 154 · see citing works
Heikkinen, S., Saqr, M., Malmberg, J., & Tedre, M. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11281-4 · Cited by 124 · see citing works
Self-citationSedrakyan, G., Mannens, E., & Verbert, K. (2018) · Journal of Computer Languages · doi:10.1016/j.jvlc.2018.11.002 · Cited by 114 · see citing works
Self-citationOuyang, F., & Zhang, L. (2024) · Educational Research Review · doi:10.1016/j.edurev.2024.100616 · Cited by 138 · see citing works
Designing in Context: Reaching Beyond Usability in Learning Analytics Dashboard Design
Ahn, J., Campos, F., Hays, M., & DiGiacomo, D. K. (2019) · Journal of Learning Analytics · doi:10.18608/jla.2019.62.5 · Cited by 109 · see citing works
Aguilar, S. J., Karabenick, S. A., Teasley, S. D., & Baek, C. (2020) · Computers & Education · doi:10.1016/j.compedu.2020.104085 · Cited by 109 · see citing works
An early warning system to identify and intervene online dropout learners
Bañeres, D., Rodríguez, M. E., Guerrero‐Roldán, A., & Cortadas, P. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-022-00371-5 · Cited by 124 · see citing works
Expectations for supporting student engagement with learning analytics: An academic path perspective
Silvola, A., Näykki, P., Kaveri, A., & Muukkonen, H. (2021) · Computers & Education · doi:10.1016/j.compedu.2021.104192 · Cited by 89 · see citing works
Yakubu, M. N., & Abubakar, A. M. (2021) · Kybernetes · doi:10.1108/k-12-2020-0865 · Cited by 88 · see citing works
Zheng, L., Long, M., Niu, J., & Zhong, L. (2023) · International Journal of Computer-Supported Collaborative Learning · doi:10.1007/s11412-023-09386-0 · Cited by 98 · see citing works
Zabolotna, K., Malmberg, J., & Järvenoja, H. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107494 · Cited by 86 · see citing works
Self-citationAlhadad, S. S. J. (2018) · Journal of Learning Analytics · doi:10.18608/jla.2018.52.5 · Cited by 85 · see citing works
Factors that affect the success of learning analytics dashboards
Park, Y., & Jo, I. (2019) · Educational Technology Research and Development · doi:10.1007/s11423-019-09693-0 · Cited by 83 · see citing works
Zheng, L., Zhong, L., & Niu, J. (2022) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2021.1883549 · Cited by 75 · see citing works
Zheng, L., Niu, J., & Zhong, L. (2021) · British Journal of Educational Technology · doi:10.1111/bjet.13156 · Cited by 78 · see citing works
Student Opinions About Personalized Recommendation and Feedback Based on Learning Analytics
Yılmaz, F. G. K., & Yılmaz, R. (2020) · Technology Knowledge and Learning · doi:10.1007/s10758-020-09460-8 · Cited by 72 · see citing works
Wang, D., & Han, H. (2021) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12502 · Cited by 55 · see citing works
Learning analytics to develop future competences in higher education: a case study
Kleimola, R., & Leppisaari, I. (2022) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-022-00318-w · Cited by 66 · see citing works
Rets, I., Herodotou, C., Bayer, V., Hlosta, M., & Rienties, B. (2021) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-021-00284-9 · Cited by 72 · see citing works
Backer, L. D., Keer, H. V., Smedt, F. D., Merchie, E., & Valcke, M. (2021) · Computers & Education · doi:10.1016/j.compedu.2021.104421 · Cited by 64 · see citing works
Investigating students' use of self‐assessments in higher education using learning analytics
Ifenthaler, D., Schumacher, C., & Kužílek, J. (2022) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12744 · Cited by 86 · see citing works
Computer‐mediated feedback for L2 learners: Challenges versus affordances
Bahari, A. (2020) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12481 · Cited by 64 · see citing works
Zheng, J., Huang, L., Li, S., Lajoie, S. P., Chen, Y., & Hmelo‐Silver, C. E. (2020) · Computers & Education · doi:10.1016/j.compedu.2020.104061 · Cited by 63 · see citing works
The equalizing effect of teacher dashboards on feedback in K-12 classrooms
Knoop-van Campen, C. A. N., Wise, A., & Molenaar, I. (2023) · Interactive Learning Environments · doi:10.1080/10494820.2021.1931346 · Cited by 58 · see citing works
van Alten, D. C. D., Phielix, C., Janssen, J. J. H. M., & Kester, L. (2020) · Computers in Human Behavior · doi:10.1016/j.chb.2020.106676 · Cited by 62 · see citing works
Eshuis, E. H., ter Vrugte, J., Anjewierden, A. A., Bollen, L., Sikken, J., & de Jong, T. (2019) · International Journal of Computer-Supported Collaborative Learning · doi:10.1007/s11412-019-09296-0 · Cited by 58 · see citing works
Reinhold, F., Leuders, T., Loibl, K., Nückles, M., Beege, M., & Boelmann, J. (2024) · Educational Psychology Review · doi:10.1007/s10648-024-09845-6 · Cited by 75 · see citing works
Üstün, A. B., Zhang, K., Yılmaz, F. G. K., & Yılmaz, R. (2022) · Journal of Research on Technology in Education · doi:10.1080/15391523.2022.2040401 · Cited by 64 · see citing works
Kia, F. S., Teasley, S. D., Hatala, M., Karabenick, S. A., & Kay, M. (2020) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3375462.3375472 · Cited by 56 · see citing works
Mejeh, M., Sarbach, L., & Hascher, T. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12510-8 · Cited by 81 · see citing works
Beyond the ICT- and sustainability hypes: A case for quality education
Visvizi, A., Daniela, L., & Chen, C.-W. (2020) · Computers in Human Behavior · doi:10.1016/j.chb.2020.106304 · Cited by 49 · see citing works
A Teacher-facing Learning Analytics Dashboard for Process-oriented Feedback in Online Learning
Dourado, R. A. D. S., Rodrigues, R. L., Júnior, N. R. F., Mello, R. F., Gomes, A. S., & Verbert, K. (2021) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3448139.3448187 · Cited by 46 · see citing works
Self-citationJivet, I., Wong, J., Scheffel, M., Torre, M. V., Specht, M. M., & Drachsler, H. (2021) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3448139.3448179 · Cited by 46 · see citing works
Deep Knowledge Tracing and Engagement with MOOCs
Mongkhonvanit, K., Kanopka, K., & Lang, D. (2019) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3303772.3303830 · Cited by 44 · see citing works
Oliveira, E., Galvao de Barba, P., & Corrin, L. (2021) · Australasian Journal of Educational Technology · doi:10.14742/ajet.6792 · Cited by 31 · see citing works
Visualizations and Dashboards for Learning Analytics: A Systematic Literature Review
Şahi̇n, M., & Ifenthaler, D. (2021) · Advances in analytics for learning and teaching · doi:10.1007/978-3-030-81222-5_1 · Cited by 43 · see citing works
Learning analytics: where information science and the learning sciences meet
Teasley, S. D. (2018) · Information and Learning Sciences · doi:10.1108/ils-06-2018-0045 · Cited by 38 · see citing works
Mahapatra, S. K. (2021) · The Asia-Pacific Education Researcher · doi:10.1007/s40299-021-00603-8 · Cited by 36 · see citing works
Ley, T., Tammets, K., Pishtari, G., Chejara, P., Kasepalu, R., Khalil, M., Saar, M., Tuvi, I., Väljataga, T., & Wasson, B. (2023) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12844 · Cited by 43 · see citing works
Ruipérez‐Valiente, J. A., Gomez, M. J., Martinez, P. A., & Kim, Y. J. (2021) · IEEE Access · doi:10.1109/access.2021.3086703 · Cited by 34 · see citing works
Khiat, H., & Vogel, S. (2022) · Journal of University Teaching and Learning Practice · doi:10.53761/1.19.2.4 · Cited by 34 · see citing works
A Visual Dashboard to Track Learning Analytics for Educational Cloud Computing
Naranjo, D. M., Prieto, J. R. F., Moltó, G., & Calatrava, A. (2019) · Sensors · doi:10.3390/s19132952 · Cited by 33 · see citing works
Malmberg, J., Fincham, O., Pijeira‐Díaz, H. J., Järvelä, S., & Gašević, D. (2021) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12529 · Cited by 46 · see citing works
Self-citationInvestigating Students’ Experiences with Collaboration Analytics for Remote Group Meetings
Zhou, Q., Suraworachet, W., Pozdniakov, S., Martínez‐Maldonado, R., Bartindale, T., Chen, P., Richardson, D., & Cukurova, M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-78292-4_38 · Cited by 32 · see citing works
Limits and Virtues of Educational Technology in Elementary School Mathematics
Christopoulos, A., Kajasilta, H., Salakoski, T., & Laakso, M. (2020) · Journal of Educational Technology Systems · doi:10.1177/0047239520908838 · Cited by 31 · see citing works
Learning Analytics in the Era of Large Language Models
Mazzullo, E., Bulut, O., Wongvorachan, T., & Tan, B. (2023) · Analytics · doi:10.3390/analytics2040046 · Cited by 34 · see citing works
Improving summary writing through formative feedback in a technology‐enhanced learning environment
Kim, M. K., & McCarthy, K. S. (2020) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12516 · Cited by 33 · see citing works
Measuring Students’ Self-Regulatory Phases in LMS with Behavior and Real-Time Self Report
Kia, F. S., Hatala, M., Baker, R. S., & Teasley, S. D. (2021) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3448139.3448164 · Cited by 29 · see citing works
Uzun, Y., Suraworachet, W., Zhou, Q., Gauthier, A., & Cukurova, M. (2025) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-025-00515-3 · Cited by 34 · see citing works
A longitudinal study of interplay between student engagement and self-regulation
Heikkinen, S., Saqr, M., Malmberg, J., & Tedre, M. (2025) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-025-00523-3 · Cited by 41 · see citing works
Self-citationArtificial Intelligence and Education
Jaakkola, H., Henno, J., Lahti, A., Jarvinen, J.-P., & Mäkelä, J. (2020) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.23919/mipro48935.2020.9245329 · Cited by 27 · see citing works
Examining Teachers’ Perception on the Impact of Positive Feedback on School Students
Câmpean, A. O., Bocoș, M. D., Roman, A. F., Rad, D., Crișan, C., Maier, M. L., Crișan-Tăușan, L., Triff, Z., Triff, D.-G., Mara, D., et al. (2024) · Education Sciences · doi:10.3390/educsci14030257 · Cited by 45 · see citing works
Yang, C. C. Y., Wu, J., & Ogata, H. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12913-7 · Cited by 25 · see citing works
Performance Prediction of Students in Higher Education Using Multi-Model Ensemble Approach
Butt, N. A., Mahmood, Z., Shakeel, K., Alfarhood, S., Safran, M., & Ashraf, I. (2023) · IEEE Access · doi:10.1109/access.2023.3336987 · Cited by 39 · see citing works
Fostering regulatory processes using computational scaffolding
Silva, L., Mendes, A. J., Gomes, A., & Fortes, G. (2023) · International Journal of Computer-Supported Collaborative Learning · doi:10.1007/s11412-023-09388-y · Cited by 24 · see citing works
Polat, E., Hopcan, S., Albayrak, E., & Durak, H. Y. (2022) · Computer Applications in Engineering Education · doi:10.1002/cae.22547 · Cited by 24 · see citing works
Song, Y., Cao, J., Yang, Y. P., & Looi, C. (2022) · International Journal of Educational Research · doi:10.1016/j.ijer.2022.101992 · Cited by 32 · see citing works
Nouri, J., Larsson, K., & Saqr, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29736-7_3 · Cited by 23 · see citing works
Possaghi, I., Vesin, B., Zhang, F.-R., Sharma, K., Knudsen, C., Bjørkum, H., & Papavlasopoulou, S. (2025) · Smart Learning Environments · doi:10.1186/s40561-025-00410-4 · Cited by 27 · see citing works
Utamachant, P., Anutariya, C., & Pongnumkul, S. (2023) · Smart Learning Environments · doi:10.1186/s40561-023-00257-7 · Cited by 23 · see citing works
A Review of Data Mining in Personalized Education: Current Trends and Future Prospects
Zhang, X., Li, H., Liu, Z., Chen, Z., Zhou, H., Rong, W., & Ouyang, Y. (2024) · Frontiers of digital education. · doi:10.1007/s44366-024-0019-6 · Cited by 23 · see citing works
Evaluating Student-Facing Learning Dashboards of Affective States
Sedrakyan, G., Leony, D., Muñoz‐Merino, P. J., Kloos, C. D., & Verbert, K. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66610-5_17 · Cited by 22 · see citing works
Self-citationFernandez-Nieto, G. M., Martinez-Maldonado, R., Echeverria, V., Kitto, K., Gašević, D., & Buckingham Shum, S. (2024) · Proceedings of the 14th Learning Analytics and Knowledge Conference · doi:10.1145/3636555.3636930 · Cited by 22 · see citing works
Krishnan, R., Nair, S., Samuel, B. S., Justin, S., Iwendi, C., Biamba, C. N., & Ibeke, E. (2022) · Sustainability · doi:10.3390/su14063378 · Cited by 21 · see citing works
Yousef, A. M. F., & Khatiry, A. R. (2021) · Interactive Learning Environments · doi:10.1080/10494820.2021.2009881 · Cited by 20 · see citing works
AppleTree system for effective computer-supported collaborative argumentation: an exploratory study
Chen, W., Tan, J. S. H., Zhang, S., Pi, Z., & Lyu, Q. (2023) · Educational Technology Research and Development · doi:10.1007/s11423-023-10258-5 · Cited by 20 · see citing works
Ahmad, W., Shokeen, R., & Raj, R. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-5538-1.ch017 · Cited by 22 · see citing works
Evaluating emotion visualizations using AffectVis, an affect-aware dashboard for students
Leony, D., Sedrakyan, G., Muñoz‐Merino, P. J., Kloos, C. D., & Verbert, K. (2017) · Journal of Research in Innovative Teaching & Learning · doi:10.1108/jrit-05-2017-0011 · Cited by 19 · see citing works
Self-citationAlZoubi, D., Kelley, J., Baran, E., Gilbert, S. B., Ilgu, A. K., & Jiang, S. (2021) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3411763.3451709 · Cited by 18 · see citing works
Chen, L., Geng, X., Lu, M., Shimada, A., & Yamada, M. (2023) · SAGE Open · doi:10.1177/21582440231192151 · Cited by 20 · see citing works
Scaffolding Feedback Literacy: Designing a Feedback Analytics Tool with Students
Jin, F. J.-Y., Maheshi, B., Martínez‐Maldonado, R., Gašević, D., & Tsai, Y. (2024) · Journal of Learning Analytics · doi:10.18608/jla.2024.8339 · Cited by 18 · see citing works
How to Design Effective Learning Analytics Indicators? A Human-Centered Design Approach
Chatti, M. A., Muslim, A., Guesmi, M., Richtscheid, F., Nasimi, D., Shahin, A., & Damera, R. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-57717-9_22 · Cited by 18 · see citing works
Sedrakyan, G., & Snoeck, M. (2017) · Communications in computer and information science · doi:10.1007/978-3-319-66302-9_4 · Cited by 18 · see citing works
Self-citationYang, Y., Majumdar, R., Li, H., Flanagan, B., & Ogata, H. (2022) · Interactive Learning Environments · doi:10.1080/10494820.2022.2101126 · Cited by 22 · see citing works
The Sequence Matters in Learning - A Systematic Literature Review
Torre, M. V., Oertel, C., & Specht, M. M. (2024) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3636555.3636880 · Cited by 18 · see citing works
Learning Analytics: a bibliometric analysis of the literature over the last decade
Azevedo, A., & Azevedo, J. (2021) · International Journal of Educational Research Open · doi:10.1016/j.ijedro.2021.100084 · Cited by 19 · see citing works
Kivimäki, V., Pesonen, J. A., Romanoff, J., Remes, H., & Ihantola, P. (2019) · Journal of Learning Analytics · doi:10.18608/jla.2019.63.13 · Cited by 18 · see citing works
Chen, A., Lian, J., Kuang, X., & Jia, J. (2025) · The Internet and Higher Education · doi:10.1016/j.iheduc.2025.101054 · Cited by 18 · see citing works
Hatala, M., Nazeri, S., & Kia, F. S. (2022) · The Internet and Higher Education · doi:10.1016/j.iheduc.2022.100881 · Cited by 15 · see citing works
The Impact of Dashboard Feedback Type on Learning Effectiveness, Focusing on Learner Differences
Wang, H., Huang, T., Zhao, Y., & Hu, S. (2023) · Sustainability · doi:10.3390/su15054474 · Cited by 16 · see citing works
Visual Learning Analytics for Educational Interventions in Primary and Secondary Schools
Mohseni, Z. A., Masiello, I., Martins, R. M., & Nordmark, S. (2024) · Journal of Learning Analytics · doi:10.18608/jla.2024.8309 · Cited by 15 · see citing works
A systematic review of visual representations for analyzing collaborative discourse
Hu, L., & Chen, G. (2021) · Educational Research Review · doi:10.1016/j.edurev.2021.100403 · Cited by 15 · see citing works
A feedback model applied in a ubiquitous microlearning environment using SECA rules
Tabares, M. S., Vallejo, P., Montoya, A., & Correa, D. (2022) · Journal of Computing in Higher Education · doi:10.1007/s12528-021-09306-x · Cited by 18 · see citing works
Zheng, L., Zhong, L., & Fan, Y. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-11588-w · Cited by 14 · see citing works
Effects of the visual analytics of peer feedback on teachers’ TPACK development in a lesson study
Wang, A., Yu, S., Wang, M., & Chen, L. (2023) · Interactive Learning Environments · doi:10.1080/10494820.2023.2204354 · Cited by 14 · see citing works
Zheng, L., Fan, Y., Huang, Z., & Gao, L. (2023) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12860 · Cited by 15 · see citing works
Ning, J., Ma, Z., Yao, J., Wang, Q., & Zhang, B. (2025) · Interactive Learning Environments · doi:10.1080/10494820.2025.2478437 · Cited by 14 · see citing works
Yin, X., Ye, J., Yu, S., Li, H., Liu, Q., & Zhao, G. (2025) · Computers & Education · doi:10.1016/j.compedu.2025.105434 · Cited by 15 · see citing works
Kannan, P., & Zapata‐Rivera, D. (2022) · Frontiers in Education · doi:10.3389/feduc.2022.913594 · Cited by 15 · see citing works
Slavi Stoyanov, A., & Kirschner, P. A. (2023) · Journal of Computer Assisted Learning · doi:10.1111/jcal.12775 · Cited by 17 · see citing works
Self-citationKaliisa, R., Gillespie, A., Herodotou, C., Kluge, A., & Rienties, B. (2021) · Advances in analytics for learning and teaching · doi:10.1007/978-3-030-81222-5_16 · Cited by 13 · see citing works
Designing Theory-Driven Analytics-Enhanced Self-Regulated Learning Applications
Chatti, M. A., Yücepur, V., Muslim, A., Guesmi, M., & Joarder, S. (2021) · Advances in analytics for learning and teaching · doi:10.1007/978-3-030-81222-5_3 · Cited by 12 · see citing works
Toyokawa, Y., Majumdar, R., Kondo, T., Horikoshi, I., & Ogata, H. (2023) · Journal of Computers in Education · doi:10.1007/s40692-023-00267-x · Cited by 16 · see citing works
- Authors
- Sedrakyan, G., Malmberg, J., Verbert, K., Järvelä, S., & Kirschner, P. A.
- Affiliations
- Ghent University (BE)
- University of Oulu (FI)
- KU Leuven (BE)
- Open University of the Netherlands (NL)
- Corresponding author
- Sedrakyan, Gayane
- Venue
- Computers in Human Behavior
- Publisher
- Elsevier BV
- Published
- 2020-06
- Volume
- 107
- Pages
- 105512
- ISSN
- 0747-5632 (ISSN-L)
- OpenAlex
- W2800204907
- Semantic Scholar
- 8b54eae687cf4632a0a6da7ac0fbd0ffca5540b9
- Cited by
- 283
- References
- 93
- Citations by year
- 2026: 21 · 2025: 33 · 2024: 45 · 2023: 49 · 2022: 44 · 2021: 42 · 2020: 26 · 2019: 16 · 2018: 4 · 2017: 3
- FWCI
- 27.79
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Computers in Human Behavior (publishedVersion)
- Lirias (KU Leuven) (acceptedVersion, other-oa)
- DSpace (Open University in the Netherlands) (submittedVersion, public-domain)
- DSpace (Open University in the Netherlands) (submittedVersion, other-oa)
- Funders
- Belgian Scientific Organization (V4.533.15N)
- Internal Funds of KU Leuven - PROFEELEARN (PDM/16/044)
- JUMO
- Academy of Finland
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Keywords
- learning analytics dashboards, learning regulation, process-oriented feedback, learning objectives, student self-regulation, dashboard design, conceptual model, visualization techniques, instructor feedback
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-10.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-10.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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