Cited by 246Online Learning and AnalyticsStudent Assessment and FeedbackIntelligent Tutoring Systems and Adaptive Learning

A systematic review of the role of learning analytics in enhancing feedback practices in higher education

Open access
Banihashem, Seyyed Kazem, Noroozi, Omid, van Ginkel, Stan, Macfadyen, Leah P., Biemans, Harm J.A.·2022·Educational Research Review

Abstract

Learning analytics (LA) offers new opportunities to enrich feedback practices in higher education, but little is understood about the ways different LA can enhance feedback practices for educators and students. This systematic literature review maps the current state of implementation of LA to improve feedback practices in technology-mediated learning environments in higher education. We used strict inclusion criteria to select relevant studies that have investigated the role of LA on feedback practices. To identify common features of LA for feedback studies, we coded relevant publications using an analytical framework that identifies four key dimensions of LA systems: what (types of data), how (analytic methods), why (objectives), and how educators and students are served by LA (stakeholders). Based on findings, we propose a conceptual framework that can guide the implementation of LA for feedback systems and also suggest future empirical research in this area.

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AI snapshot

Written from the paper’s full text.

TL;DR

This systematic review maps 46 empirical studies on learning analytics (LA) for feedback in higher education using Chatti et al.’s what/how/why/who framework. It proposes an evidence-based conceptual framework in which stakeholder and objective should guide data collection and analytic method choices for LA-based feedback.

Key findings

  • LA feedback objectives most often targeted reflection (20 studies, 43%), personalization (13, 28%), prediction (11, 24%), assessment (11, 24%), adaptation (10, 22%), recommendation (10, 22%), and monitoring (6, 13%).
  • LA-supported feedback was linked to pedagogical outcomes including academic performance (17, 37%), engagement/community of inquiry (11, 24%), self-regulation (8, 17%), motivation/emotion (8, 17%), time management (3, 6%), perception of feedback (2, 4%), cognitive load (2, 4%), reduced procrastination (1, 2%), satisfaction (1, 2%), reflective thinking (1, 2%), and transactional distance (1, 2%).
  • Trace data was the dominant data type (23 codes, 36%), followed by assessment data (12, 18%), academic history data (11, 17%), and survey data (8, 12%); information visualization was the most common method (22 codes, 50%), then data mining (11, 25%), text analysis (8, 18%), and social network analysis (3, 7%).
  • Students were stakeholders in all included publications, while educators were mentioned in 15 of 46; the proposed framework orders decisions as stakeholder → objective → data → method, with analytics levels (descriptive, diagnostic, predictive, prescriptive) tied to different feedback services.

Methods

  • PRISMA systematic review; searched Web of Science, Scopus, ERIC, IEEE, and LAK proceedings with terms (“learning analytics” OR analytic* OR “data mining”) AND (feedback OR feedforward OR “formative assessment”); inclusion required English, 2011–2022, peer-reviewed journal articles, empirical studies, and higher education settings.
  • From 1,318 initial records, after removing duplicates/non-peer-reviewed and screening, 46 studies met criteria and passed quality appraisal using Theelen et al. (2019) criteria; all 46 met the minimum methodology score.
  • Publications were coded in ATLAS.ti 9 using a four-category scheme (what/how/why/who); inter-rater reliability on two randomly selected papers was 91% agreement (κ = 0.84, p < .001).

Limitations

  • Stated: limited to empirical studies, so relevant reviews/conceptual papers may be missed; selected databases may miss unindexed studies; publication bias likely; findings are not generalizable to K-12; the review did not identify gaps in feedback features/purposes or investigate effect sizes of different data types.
  • Stated: no distinction was made between educator feedback and peer feedback, and no analysis of how LA may affect affective, cognitive, and constructive features of feedback quality.
  • Evident from the text: the coding scheme in Table 3 assigns the same code “C5” to both Personalization and Recommendation, and some table reference lists appear truncated or misaligned; these presentation issues may affect transparency of coding/reporting.

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Registry

Crossref · 10.1016/j.edurev.2022.100489

Cites

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Claims the paper makes

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  • Findingown data
    § 4.1. RQ1: for what reasons is LA used in feedback studies in higher education? · p. 8

    Prediction (11 studies, 24%).

    “Prediction | 11 | 24% |”
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    § 4.1. RQ1: for what reasons is LA used in feedback studies in higher education? · p. 8

    Self-regulation in 8 studies (17%).

    “Self-regulation | 8 | 17% |”
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    § 4.1. RQ1: for what reasons is LA used in feedback studies in higher education? · p. 8

    Assessment (11 studies, 24%).

    “Assessment | 11 | 24% |”
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    § 4.2.1. Data used for LA feedback intervention · p. 11

    Trace data was the dominant data type used by LA to support feedback in higher education.

    “Trace data : According to the reviewed studies, this is the dominant data type used by LA to support feedback in higher education.”
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    § 4.2. RQ2: what types of data does LA use to support feedback in higher education? · p. 11

    Assessment data accounted for 12 codes (18%) in the review.

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    Findingreasoning
    § 4.2. RQ2: what types of data does LA use to support feedback in higher education? · p. 10

    The data groups were divided into two main categories: data used for LA feedback intervention and data used for researching LA feedback intervention.

    “we divided these groups of data into two main category including (a) data used for LA feedback intervention and (b) data used for researching LA feedback intervention”
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    § 3.5. Included publications · p. 5

    Study context varied from medicine to statistics, but studies of courses in computer science were most common (N=11, 24%).

    “Study context varied - from medicine to statistics -but studies of courses in the field of computer science were found most common (N = 11, 24%).”
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    § 3.5. Included publications · p. 5

    Studies from The Netherlands (N=5, 11%), Turkey (N=5, 11%), and The UK (N=3, 8%) were the next most common.

    “Studies from The Netherlands (N = 5, 11%), Turkey (N = 5, 11%), and The UK (N = 3, 8%) were the next most common.”
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    § 4.1. RQ1: for what reasons is LA used in feedback studies in higher education? · p. 8

    Satisfaction in 1 study (2%).

    “Satisfaction | 1 | 2% |”
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    § 4.1. RQ1: for what reasons is LA used in feedback studies in higher education? · p. 8

    Recommendation (10 studies, 22%).

    “Recommendation | 10 | 22% |”
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    § 3.5. Included publications · p. 5

    Most included papers were published since 2019 (N=37, 80%).

    “As shown in Table 2, most of the included papers have been published since 2019 (N = 37, 80%).”
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    § 3.5. Included publications · p. 5

    Most studies were quantitative (N=28, 61%), alongside 11 qualitative studies (24%), and 7 mixed methods studies (15%).

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    § 3.5. Included publications · p. 5

    A considerable number of papers reported on studies conducted in Australia of the OnTask platform (N=10, 22%).

    “A considerable number of papers reported on studies conducted in Australia of the OnTask platform (N = 10, 22%).”
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    § 4.1. RQ1: for what reasons is LA used in feedback studies in higher education? · p. 8

    Reduce procrastination in 1 study (2%).

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    § 4.1. RQ1: for what reasons is LA used in feedback studies in higher education? · p. 8

    Reflective thinking skills in 1 study (2%).

    “Reflective thinking skills | 1 | 2% |”
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    § 4.1. RQ1: for what reasons is LA used in feedback studies in higher education? · p. 8

    Academic performance was a pedagogical objective achieved through LA-supported feedback in 17 studies (37%).

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    § 4.1. RQ1: for what reasons is LA used in feedback studies in higher education? · p. 8

    Transactional distance in 1 study (2%).

    “Transactional distance | 1 | 2% |”
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    § 3.3. Identification of relevant publications · p. 4

    The initial search identified 1318 papers.

    “In the first phase of screening in selected databases, we identified a total of 1318 papers (Web of Science (n = 807), Scopus (n = 200), ERIC (n = 101), IEEE (n = 166), and LAK Conference contributions (n = 53)).”
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    § 3.4. Quality appraisal · p. 4

    All 46 studies met the minimum quality appraisal score and were included in the final analysis.

    “All 46 studies in fact met the minimum score for the quality of methodology and were therefore included in the final analysis.”
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    Findingcitation
    § 4.2.2. Data used for researching LA feedback intervention · p. 12

    LA-based feedback underpinned by survey data can affect student cognitive load, community of inquiry, reflective thinking skills, motivation, transactional distance, and engagement.

    “Reviewed publications revealed that LA-based feedback underpinned by survey data can affect student cognitive load (Sun et al., 2019; Zheng et al., 2021), the community of inquiry, reflective thinking skills (Yilmaz, 2020), motivation, and transactional distance (Yilmaz &amp; Yilmaz, 2020), and engagement (Sun et al., 2019; Yilmaz &amp; Yilmaz, 2021, pp. 1 -12).”
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    § 4.1.1. LA objectives for enhancing feedback practices in higher education · p. 9

    In the reviewed studies, LA was used as a recommender system to guide learners and suggest useful course material.

    “In a few of the studies we reviewed, LA is used as a recommender system to guide learners and the learning process (Sedrakyan et al., 2016), and suggest useful course material (Azcona et al., 2019).”
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    § 4.1.2. Pedagogical objectives achieved through LA-supported feedback practices · p. 10

    LA-based feedback can influence students' reflective thinking skills and transactional distance.

    “Furthermore, LA-based feedback can influence students ' reflective thinking skills and transactional distance (Yilmaz, 2020; Yilmaz et al., 2020).”
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    § 4.2.1. Data used for LA feedback intervention · p. 12

    Assessment data is often used by LA in feedback studies to provide recommendations, assessment, predictive feedback, and personalized feedback.

    “Assessment data is often used by LA in feedback studies to provide recommendations (Lewis et al., 2021; Tempelaar et al., 2018), assessment (Chaudy &amp; Connolly, 2019; Tempelaar et al., 2018), predictive feedback (Arnold &amp; Pistilli, 2012; Baneres et al., 2019), and personalized feedback (Iraj et al., 2020; Lim, Gentili, et al., 2021).”
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    § 4.2.2. Data used for researching LA feedback intervention · p. 12

    Survey data-based LA can foster predictive feedback, recommendation, reflective feedback, and assessment.

    “Selected studies suggest that survey data-based LA can foster predictive feedback (Broos et al., 2020), recommendation (Perikos et al., 2017), reflective feedback (Smith, 2020; Tsai et al., 2021), and assessment (Dalla Valle et al., 2018).”
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    § 4.2.1. Data used for LA feedback intervention · p. 12

    Demographic data is one of the most commonly employed data types in LA studies.

    “This data type is one of the most commonly employed in LA studies (Azcona et al., 2019; Tempelaar et al., 2015).”
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    § 4.1.2. Pedagogical objectives achieved through LA-supported feedback practices · p. 10

    Students with higher self-efficacy more appreciated LA-based feedback and had positive experiences with it.

    “Students with higher self-efficacy more appreciated LA-based feedback and had positive experiences with it (Tsai et al., 2021).”
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    § 4.2.1. Data used for LA feedback intervention · p. 12

    Trace data can be used to generate reflective feedback, personalized feedback, and predictive feedback.

    “Reviewed studies indicate that such data can be used to generate different types of feedback such as reflective feedback”
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    § 4.1.2. Pedagogical objectives achieved through LA-supported feedback practices · p. 10

    Using LA-based feedback has made it easier for educators to monitor students' learning progress and provide personalized and real-time feedback.

    “However, using LA-based feedback has made it easier for educators to monitor their students ' learning progress and to provide personalized and real-time feedback (Shibani et al., 2020).”
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    § 4.1.2. Pedagogical objectives achieved through LA-supported feedback practices · p. 10

    LA-based feedback can impact students' motivation and engagement.

    “We found that LA-based feedback can also impact students ' motivation (e.g., Smith, 2020) and engagement (e.g., Iraj et al., 2020).”
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    § 4.1.2. Pedagogical objectives achieved through LA-supported feedback practices · p. 10

    If LA-based feedback relies only on warning systems, it can increase mental load, while adding encouragement can lead to higher emotional and cognitive engagement.

    “If LA-based feedback relies only on warning systems, then it can increase the mental load, while putting warning systems with the encouragement of what to do can lead to higher emotional and cognitive engagement (Sun et al., 2019).”
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    § 4.1.2. Pedagogical objectives achieved through LA-supported feedback practices · p. 9

    LA-supported feedback entails real-time reflections on students' performance and personalized and reflective feedback can help students be more aware of their own learning processes to regulate their learning.

    “LA-supported feedback entails real-time reflections on students ' performance during their learning processes and that personalized and reflective feedback can help students to be more aware of their own learning processes in order to regulate their learning (Lim, Gasevic, et al., 2021).”
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    § 4.2.2. Data used for researching LA feedback intervention · p. 12

    Interview data is often supplementary data for other types of data and can offer more in-depth information.

    “It is often supplementary data for other types of data that can be mined from LMS or other online platforms and can offer more in-depth information.”
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    § 4.2.1. Data used for LA feedback intervention · p. 12

    Academic history data is often combined with other types of data to provide reflective and predictive feedback, and formative assessment.

    “in LA feedback studies, it is often combined with other types of data to provide reflective (Lim, Gentili, et al., 2021) and predictive feedback (Azcona et al., 2019), and formative assessment (Tempelaar et al., 2018).”
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    § 4.4. RQ4: how does LA support feedback stakeholders in higher education? · p. 14

    In all included publications, students were mentioned as the LA-supported feedback stakeholder, followed by educators mentioned in 15 out of 46 included studies.

    “As our review revealed, unsurprisingly, in all included publications, students were mentioned as the LA-supported feedback stakeholder, followed by educators mentioned in 15 out 46 included studies.”
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    § discussed below. · p. 13

    Information visualization methods seem to be commonly used alongside other LA methods to identify students at risk of failure and to understand student perceptions and interactions.

    “Information visualization methods seem to be commonly used alongside other LA methods to identify students at risk of failure and to understand student perceptions and interactions.”
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    § 4.4. RQ4: how does LA support feedback stakeholders in higher education? · p. 15

    At the descriptive level, LA-based feedback provides reflections on students learning activities and learning progress.

    “while at the descriptive level, LA-based feedback provides reflections on students learning activities and learning progress”
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    § 4.4. RQ4: how does LA support feedback stakeholders in higher education? · p. 15

    At the predictive level, LA is used to predict students' retention or dropout by using data mining methods.

    “at the predictive level, is to predict students ' retention or dropout by using data mining methods.”
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    § 4.4. RQ4: how does LA support feedback stakeholders in higher education? · p. 15

    LA-based feedback services assist students with improving their reflective thinking skills.

    “but also assist them with improving their reflective thinking skills (e.g. Yilmaz, 2020)”
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    § 4.4. RQ4: how does LA support feedback stakeholders in higher education? · p. 15

    LA-based feedback has a reminder system that helps students to be more focused and less procrastinated.

    “and also has a reminder system that helps students to be more focused and less procrastinated (e.g. Lim et al., 2020).”
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    § discussed below. · p. 13

    Methods used to support feedback include predictive modelling, classification, clustering, Bayesian network analysis, natural language processing, process mining, relationship mining, support vector machine, discovery model, machine learning, and neural network.

    “Methods used to support feedback include predictive modelling (Azcona et al., 2019; Tempelaar et al., 2015), classifi­ cation (Baneres et al., 2019), clustering (Lim, Gasevic, et al., 2021; Tempelaar et al., 2018; Tsai et al., 2021), Bayesian network analysis (Choi &amp; Cho, 2020), natural language processing (Perikos et al., 2017), process mining (Sedrakyan et al., 2016), relationship mining (T”
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    § 6. A conceptual framework to guide the use of LA for feedback practices in higher education · p. 16

    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).”
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    § 5. Discussion · p. 15

    For students, LA mostly supported students to be informed about how they are doing, reflect on their own learning process to adapt their learning based on learning needs.

    “LA mostly supported students to be informed about how they are doing, reflect on their own learning process to adapt their learning based on the learning needs.”
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    § 5. Discussion · p. 15

    For educators, LA mainly provided insights into tracking students learning activities for timely intervention, assessing students learning progress, and early identifying students at risk.

    “While for educators, LA mainly provided insights into tracking students learning activities for timely intervention, assessing students learning progress, and early identifying students ' who are at risk”
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    § 5. Discussion · p. 15

    Many projects employed multiple methods.

    “It is worth noting that many projects employed multiple methods.”
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    § 5. Discussion · p. 15

    Additional data types employed are also 'about students', confirming that most LA for feedback work is student-focused and seeks to serve students as the primary stakeholders.

    “Additional data types employed - demographic, assessment, and academic history data -are also 'about students ' , confirming that most LA for feedback work is student-focused and seeks to serve students as the primary stakeholders.”
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    Premisecitation
    § 1. Introduction · p. 1

    High-quality feedback contributes to authentic learning by promoting student metacognitive skills and providing clear indicators of performance and areas for further improvement.

    “Studies have shown that high-quality feedback contributes to authentic learning by promoting student metacognitive skills, and by providing clear indicators of performance and areas for further improvement (e.g. Callender et al., 2016; Molin et al., 2020).”
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    § 1. Introduction · p. 1

    Effective feedback is a key feature of quality teaching.

    “Effective feedback represents one of the key features of quality teaching (Ramsden, 2003).”
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    § 1.1. Learning analytics · p. 2

    Feedback as a core pedagogical practice has gained more attention in LA.

    “Consequently, feedback as a core pedagogical practice has therefore gained more attention (e.g. Er et al., 2021; Misiejuk et al., 2021).”
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    § 1.3. Learning analytics and feedback · p. 2

    Van der Schaaf et al. (2017) reported that LA enhanced the quality and efficiency of feedback in professional education.

    “Van der Schaaf et al. (2017) reported that LA enhanced the quality and efficiency of feedback in professional education.”
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    § Educational Research Review · p. 2

    Cavalcanti et al. (2021) provide insights into the role of automated feedback in students' performance and instructors' workload.

    “For example, the recent study by Cavalcanti et al. (2021) provide insights into the role of automated feedback in students ' performance and instructors ' workload.”
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    § 2. Conceptualizing the review · p. 3

    Various analytics methods can be applied at different levels, depending on data type and LA goals.

    “Various analytics methods (techniques) can be applied at different levels, depending on data type and LA goals (Chatti et al., 2012);”
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    Study design and conductcitation
    § 3. Method · p. 4

    Theelen et al.'s (2019) critical appraisal strategy was applied to review the quality of publications.

    “Then, Theelen et al. ' s (2019) critical appraisal strategy was applied to review the quality of publications.”
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    § 2. Conceptualizing the review · p. 3

    LA consists of four dimensions: what (types of data), how (analytic methods), why (objectives), and who (stakeholders).

    “According to this model, LA consists of four dimensions including (1) what (what types of data does the system capture and analyze), (2) how (how does the system perform analytics), (3) why (for what reasons does the system gather and analyze data), and (4) who (who is served by the analytics) (Chatti et al., 2012).”
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    § 3.6. Analytic strategy · p. 7

    The Kappa results showed 91 percent agreement between the two coders (κ = 0.84, p < .001), which confirms the high consistency and reliability of the coding.

    “The Kappa results showed 91 percent agreement between the two coders ( κ = 0.84, p &lt; .001), which confirms the high consistency and reliability of the coding.”
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    § 1.1. Learning analytics · p. 2

    The interdisciplinary field of LA has borrowed concepts and ideas from diverse disciplines including computer science, learning science, statistics, data mining, psychology, and pedagogy.

    “The interdisciplinary field of LA has borrowed concepts and ideas from diverse disciplines, including computer science, learning science, statistics, data mining, psychology, and pedagogy (Chatti et al., 2012).”
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    Premisecitation
    § 1.3. Learning analytics and feedback · p. 3

    It has been suggested that LA should be an essential component of feedback design in data-rich environments.

    “and it has been suggested that LA should therefore be an essential component of feedback design in data-rich environments (Er et al., 2021; Pardo, 2018).”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites Er et al., 2021

      Not checked in the free run.

    • cites Pardo, 2018 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation
    § 1.3. Learning analytics and feedback · p. 2

    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”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites Sedrakyan et al., 2020 source

      Not checked in the free run.

    • cites Lim et al., 2021

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § 2. Conceptualizing the review · p. 3

    LA can serve different stakeholders - students, educators, tutors, mentors, institutions, researchers, and/or system designers.

    “Finally, LA can serve different stakeholders - students, educators, tutors, mentors, institutions, re­ searchers, and/or system designers (Chatti et al., 2012), and no LA system can serve all stakeholders.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Chatti et al., 2012 source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Study design and conductcitation
    § 3. Method · p. 4

    This study adopted the PRISMA methodology for the systematic review.

    “This study adopted the PRISMA methodology for the systematic review (Moher et al., 2009) to undertake a transparent process of search strategy development, definition of inclusion criteria, and identification of relevant publications.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Moher et al., 2009 source

      Not checked in the free run.

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductnone given
    § 3. Method · p. 4

    A coding scheme was developed based on the suggested theoretical framework and used to analyze the publications.

    “Next, a coding scheme was developed based on the suggested theoretical framework and used to analyze these publications.”
    Quote in the paperSubstantial claim
  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductreasoning
    § 3.2. Criteria for inclusion · p. 4

    Inclusion criteria included English language, publication from 2011 to 2022, and peer-reviewed articles in scholarly journals.

    “We included only studies published in the English language (because most of the publications in this area of research are written in English). We only included publications from 2011 to 2022, since LA as a new field of study emerged in 2011. Finally, to ensure the originality, validity, and quality of selected publications, we included only peer-reviewed articles published in scholarly journals.”
    Quote in the paperSubstantial claim
  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § 2. Conceptualizing the review · p. 3

    No LA system can serve all stakeholders.

    “and no LA system can serve all stakeholders.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Chatti et al., 2012 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.

    Premisecitation
    § 1.1. Learning analytics · p. 2

    LA seeks to translate learning data into knowledge that can give students insight into their own learning and enable educators to make appropriate and evidence-based interventions for the improvement of teaching and learning.

    “LA seeks to translate learning data into knowledge (Wong et al., 2019) that can give students insight into their own learning, and enable educators to make appropriate and evidence-based interventions for the improvement of teaching and learning (Wise, 2014).”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites Wong et al., 2019

      Not checked in the free run.

    • cites Wise, 2014

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § 1.3. Learning analytics and feedback · p. 3

    A further interesting area of work involves the use of feedback analytics to support student academic writing.

    “A further interesting area of work involves the use of feedback analytics to support student academic writing (Gibson et al., 2017; Knight et al., 2020).”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites Gibson et al., 2017 source

      Not checked in the free run.

    • cites Knight et al., 2020 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § 4.2.1. Data used for LA feedback intervention · p. 12

    Initial LA tools were too reliant on demographic data for generating early-warning systems.

    “it has been criticized that initial LA tools were too reliant on this type of data for generating early-warning systems (Ga ˇ sevi ´ c et al., 2016; Lim, Gentili, et al., 2021).”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites Ga ˇ sevi ´ c et al., 2016

      Not checked in the free run.

    • cites Lim, Gentili, et al., 2021

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationreasoning
    § 4.2.2. Data used for researching LA feedback intervention · p. 12

    Data mining methods are applied mainly on survey data.

    “For this reason, data mining methods are applied mainly on survey data.”
    Quote in the paperNot a substantial claim
  • May combine two claims.

    Interpretationcitation
    § 4.1.2. Pedagogical objectives achieved through LA-supported feedback practices · p. 10

    Insights from SNA help teachers have an overview of students' interactions and identify less engaged students to support them.

    “These insights help teachers to have an overview of students ' interactions with each other and to identify which student is less engaged to support him/her.”
    Quote in the paperNot a substantial claim
  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § 4.1.2. Pedagogical objectives achieved through LA-supported feedback practices · p. 10

    Students with high self-efficacy are more able to execute behaviors necessary to produce a specific performance.

    “This could be due to the reason that students with high self-efficacy are more able to execute behaviors that are necessary to produce a specific performance (Bandura et al., 1999).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Bandura et al., 1999 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § discussed below. · p. 13

    Data mining methods mainly seek to discover patterns, correlations, and anomalies in large volumes of data.

    “Generally, such methods mainly seek to discover patterns, correlations, and anomalies in large volumes of data (Lee &amp; Siau, 2001).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Lee & Siau, 2001

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationnone given
    § 4.4. RQ4: how does LA support feedback stakeholders in higher education? · p. 15

    LA-based recommendations and personalized feedback typically refer to LA at the prescriptive level.

    “Furthermore, LA-based recommendations and personalized feedback typically refer to LA at the prescriptive level.”
    Quote in the paperNot a substantial claim
  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationcitation
    § 4.4. RQ4: how does LA support feedback stakeholders in higher education? · p. 15

    LA services are provided at different levels of analytics according to Gartner Analytics Ascendancy Model.

    “this figure illustrates that LA services are provided at different levels of analytics according to Gartner Analytics Ascendancy Model (GAAM) (Eriksson et al., 2020; Maoz, 2013).”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites Eriksson et al., 2020 source

      Not checked in the free run.

    • cites Maoz, 2013

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation
    § discussed below. · p. 13

    LA in predictive analytics level tells what will happen, while in prescriptive analytics level, LA recommends actions and interventions for the future.

    “LA in predictive analytics level tells us ' what will happen ' , while in the prescriptive analytics level, LA recommends actions and interventions for the future (Turkington et al., 2018).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Turkington et al., 2018

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning
    § discussed below. · p. 14

    SNA methods are intended to be used at descriptive and diagnostic levels.

    “Based on this explanation, it is expected that SNA methods are intended to be used at descriptive and diagnostic levels.”
    Quote in the paperNot a substantial claim
  • May combine two claims.

    Interpretationreasoning
    § discussed below. · p. 14

    Network analysis methods appear to be helpful in understanding how students perceive feedback and how they reflect on their learning process alongside exploring how students interact with each other.

    “Network analysis methods appear to be helpful in understanding how students perceive feedback and how they reflect on their learning process alongside exploring how students interact with each other.”
    Quote in the paperNot a substantial claim
  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationreasoning
    § 4.4. RQ4: how does LA support feedback stakeholders in higher education? · p. 15

    Predictive feedback is expected to be provided at the predictive level.

    “This implies that predictive feedback is expected to be provided at the predictive level.”
    Quote in the paperNot a substantial claim
  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning
    § 8. Conclusions · p. 17

    Effective and appropriate use of learning analytics to empower feedback practices in higher education requires the proper choice of analytics.

    “the effective and appropriate use of LA to empower feedback practices in higher education requires a clear understanding of the educators ' and students ' pedagogical needs for LA support, required data to meet these needs, and the proper choice of analytics.”
    Quote in the paperNot a substantial claim
  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data
    § 7. Limitations and suggestions for future research and practice · p. 17

    The study did not make a distinction between educator feedback and peer feedback.

    “In our findings, we did not make a distinction between educator feedback and peer feedback and how differences between these two types of feedback may lead to different use of LA in higher education contexts.”
    Quote in the paperNot a substantial claim
  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data
    § 7. Limitations and suggestions for future research and practice · p. 16

    The study did not investigate the effect size of different types of data to see which type can more support feedback activities.

    “We also did not investigate the effect size of different types of data to see which certain type of data can more support feedback activities in higher education settings.”
    Quote in the paperNot a substantial claim
  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductcitation
    § 7. Limitations and suggestions for future research and practice · p. 16

    There is a publication bias which means that research with null findings is often not published.

    “Third, it is also important to acknowledge that there is a publication bias which means that research with null findings is often not published (Larrabee S ø nderlund et al., 2019).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Larrabee S ø nderlund et al., 2019

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation
    § 7. Limitations and suggestions for future research and practice · p. 17

    There is a difference between teacher feedback and peer feedback as teachers are more subject-matter experts and need less support in providing feedback.

    “According to the literature, there is a difference between teacher feedback and peer feedback as teachers are more subject-matter experts with lots of feedback experiences and knowledge, and compared to students they need less support in providing feedback (Gielen et al., 2010; Paulus, 1999).”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites Gielen et al., 2010 source

      Not checked in the free run.

    • cites Paulus, 1999 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § 6. A conceptual framework to guide the use of LA for feedback practices in higher education · p. 16

    The hierarchy of steps incorporated into the conceptual framework is in line with the findings of prior studies.

    “This hierarchy of steps incorporated into our conceptual framework is in line with the findings of prior studies (e.g. Banihashem et al., 2022; Chatti et al., 2012; Elias, 2011, pp. 1 -22; Clow, 2012).”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites Banihashem et al., 2022 source

      Not checked in the free run.

    • cites Chatti et al., 2012 source

      Not checked in the free run.

    • cites Elias, 2011

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Premisecitation
    § 6. A conceptual framework to guide the use of LA for feedback practices in higher education · p. 16

    Defining the recipient of the LA and the goal of the use of LA would make it clear what kind of data should be captured and analyzed.

    “Defining the recipient of the LA and the goal of the use of LA would make it clear what kind of data should be captured and analyzed (Chatti et al., 2012).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Chatti et al., 2012 source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning
    § 5. Discussion · p. 15

    For educators, LA was mainly used at diagnostic and predictive levels, while for students, LA was employed at descriptive and prescriptive levels.

    “These findings suggest that for educators, LA was mainly used at diagnostic and predictive levels, while for students, LA was employed at descriptive and prescriptive levels.”
    Quote in the paperSubstantial claim
  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given
    § 7. Limitations and suggestions for future research and practice · p. 17

    The study did not provide insights into how different categories of data are connected to different LA objectives and methods.

    “However, we did not provide insights into how different categories of data are connected to different LA objectives and methods for feedback purposes in higher education.”
    Quote in the paperNot a substantial claim
  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning
    § 8. Conclusions · p. 17

    Effective and appropriate use of learning analytics to empower feedback practices in higher education requires the required data to meet educators' and students' pedagogical needs.

    “the effective and appropriate use of LA to empower feedback practices in higher education requires a clear understanding of the educators ' and students ' pedagogical needs for LA support, required data to meet these needs, and the proper choice of analytics.”
    Quote in the paperNot a substantial claim
  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning
    § 8. Conclusions · p. 17

    Effective and appropriate use of learning analytics to empower feedback practices in higher education requires a clear understanding of educators' and students' pedagogical needs for learning analytics support.

    “the effective and appropriate use of LA to empower feedback practices in higher education requires a clear understanding of the educators ' and students ' pedagogical needs for LA support, required data to meet these needs, and the proper choice of analytics.”
    Quote in the paperNot a substantial claim
  • May combine two claims.

    Interpretationreasoning
    § 5. Discussion · p. 15

    LA role in the improvement of feedback practices in higher education has not been tightened to feedback itself, but also has evolved beyond the authors' first anticipation based on their proposed model.

    “These findings imply that LA role in the improvement of feedback practices in higher education have not been tightened to feedback itself, but also has evolved beyond our first anticipation based on our proposed model.”
    Quote in the paperSubstantial claim
  • May combine two claims.

    Study design and conductreasoning
    § 7. Limitations and suggestions for future research and practice · p. 16

    The review was limited to empirical studies, and some noteworthy reviews and conceptual papers may have been missed.

    “First, our review was limited to empirical studies for sake of reporting only authentic findings, and we recognize that some noteworthy reviews and conceptual papers may have been missed.”
    Quote in the paperNot a substantial claim
  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given
    § 7. Limitations and suggestions for future research and practice · p. 16

    The selected set of literature databases may have missed studies not indexed in them.

    “we acknowledge that we may have missed studies not indexed in our selected databases.”
    Quote in the paperNot a substantial claim
  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data
    § 7. Limitations and suggestions for future research and practice · p. 16

    The study did not provide information on identifying gaps regarding feedback features and purposes not addressed by LA in higher education.

    “In this study, we did not provide information on identifying the gaps regarding the feedback features and purposes that have not been addressed by LA in higher education.”
    Quote in the paperNot a substantial claim
  • May combine two claims.

    Study design and conductreasoning
    § 7. Limitations and suggestions for future research and practice · p. 16

    The study does not investigate LA-based feedback studies in K-12 educational contexts, so findings may not be generalizable to all modes of educational contexts.

    “Fourth, while this study expands our understanding of how feedback in higher education is influenced by LA, it does not investigate LA-based feedback studies in any K-12 educational contexts. Our findings may therefore not be generalizable to all modes of educational contexts.”
    Quote in the paperNot a substantial claim
  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data
    § 7. Limitations and suggestions for future research and practice · p. 17

    The study's findings do not provide insights into the role of LA in features of feedback.

    “In addition, our findings do not provide insights into the role of LA in features of feedback, for example how LA can influence affective, cognitive, and constructive features of feedback quality.”
    Quote in the paperNot a substantial claim
  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation
    § 7. Limitations and suggestions for future research and practice · p. 17

    Providing feedback for students causes a more cognitive workload compared to teachers.

    “Providing feedback for students causes a more cognitive workload compared to teachers (Noroozi et al., 2022).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Noroozi et al., 2022

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Interpretationreasoning
    § 8. Conclusions · p. 17

    Following the steps of understanding pedagogical needs, providing required data, and choosing proper analytics leads to learning analytics being delivered properly to support feedback.

    “By following these steps, we can expect to see LA is delivered properly to support feedback.”
    Quote in the paperSubstantial claim
89 of 89
  • The paper relies on this without support.

    Only few review studies have focused on providing an overview of the role of LA in feedback.

    “However, only few review studies have focused on providing an overview of the role of LA in feedback.”
  • The paper relies on this without support.

    According to the literature, there is no clear understanding of how LA tools can support feedback practices and how LA's use for feedback purposes in higher education can be guided.

    “According to the literature, there is no clear understanding of how LA tools can support feedback practices and how LA ' s use for feedback purposes in higher education can be guided.”
  • The paper relies on this without support.

    To date, no conceptual frameworks have been offered to guide such role of LA for the design of feedback practices.

    “To date, however, no conceptual frameworks have been offered to guide such role of LA for the design of feedback practices.”
  • The paper relies on this without support.

    There is no overview at which levels social network analysis methods can be used.

    “For example, there is no overview at which levels social network analysis methods can be used.”
  • The paper relies on this without support.

    For feedback practices in higher education, educators and students tend to be the key stakeholders.

    “It might be clear that for feedback practices in higher education, educators and students tend to be the key stakeholders”
  • The paper relies on this without support.

    Little is known about how educators and students have benefitted from LA applications in higher education.

    “however, little is known about how educators and students have benefitted from LA applications in higher education?”
  • The paper relies on this without support.

    Survey data usually contain high validity because they have already been validated by other scholars.

    “What is important to note about the survey data is that this type of data usually contain a high validity, since they have already been validated by other scholars.”
  • The authors state this as an assumption.

    The selected set of literature databases covers the most relevant publications.

    “while we feel that our selected set of literature databases covers the most relevant publications”
8 of 8

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?

  • The quality of peer feedback content can be further enriched when combined with feedback from other sources, such as tutors and automated systems employing AI and learning analytics.

    “Furthermore, the quality of peer feedback content can be further enriched when combined with feedback from other sources, such as tutors and automated systems employing AI and learning analytics (Banihashem et al., 2022; Farrokhnia et al., 2023; Van den Boom et al., 2007).”

    From A systematic review of the key components of online peer feedback practices in higher education (2024) · cites this paper as Banihashem et al., 2022

    Section 1.3 (p. 2) reports that learning analytics can support meaningful, timely feedback, so the source supports the general role of analytics in feedback. But the Limitations section (p. 17) says the review did not distinguish peer from educator feedback and provides no insight into features of feedback quality. It does not establish that combining peer feedback with tutor or automated feedback enriches the quality of peer feedback content. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • UK study percentage is too high

    Minor

    Three of 46 studies is about 6.5%, which rounds to 7%, not 8%.

    Reported 8 · recomputed 6.521739

  • Conference papers included despite a journals-only criterion

    Minor

    The inclusion criteria restrict publications to scholarly journal articles, but the final set includes six conference-proceedings papers.

2 of 2

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Banihashem, S. K., Noroozi, O., van Ginkel, S., Macfadyen, L. P., & Biemans, H. J. A.
Affiliations
  • Open University of the Netherlands (NL)
  • Wageningen University & Research (NL)
  • University of British Columbia (CA)
Corresponding author
Banihashem, Seyyed Kazem
Venue
Educational Research Review
Publisher
Elsevier BV
Published
2022-11
Volume
37
Pages
100489
ISSN
1747-938X (ISSN-L)
OpenAlex
W4308066047
Cited by
246
References
138
Citations by year
2026: 74 · 2025: 74 · 2024: 57 · 2023: 40 · 2022: 1
FWCI
7.05
Type
journal article
Language
en
Open access
Locations
  • Educational Research Review (publishedVersion, cc-by)
  • Socio-Environmental Systems Modeling (submittedVersion, cc-by)
  • DSpace (Open University in the Netherlands) (submittedVersion, cc-by)
  • Data Archiving and Networked Services (DANS) (submittedVersion, cc-by)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
  • Social Sciences › Education › Student Assessment and Feedback
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
Keywords
learning analytics, feedback practices, higher education, technology-mediated learning environments, conceptual framework, student feedback, analytical methods, stakeholders

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