Learning Analytics Lens: Improving Quality of Higher Education

May combine two claims.

Learning analytics has assisted in discovering undesirable learning behaviors and learners' emotional states.

“discovering undesirable learning behaviors and their emotional states”

May combine two claims.

Learning analytics has assisted in monitoring and motivating learners.

“It has also helped in monitoring and motivating them”

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

Almost all authors who submitted papers to the Third Conference on Learning Analytics and Knowledge were from Europe and North American countries.

“In the Third Conference on “Learning Analytics and Knowledge,” almost all the authors who submitted papers are from Europe and North American countries [101].”

• cites [101]Not checked yet

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Classified as probably not a substantial claim.

Learning analytics reveals concealed patterns, correlations, and other insights concerning learners and educators within educational big data.

“Learning analytics uncover hidden patterns, correlation, and other insights about learners and educators in educational big data”

May combine two claims.

Learning analytics has a strong effect on changing educational practices and upgrading teaching and learning practices.

“it is observed that LA has a strong effect on changing educational practices and upgrading teaching and learning practices.”

• AssumptionRests on an assumption: The literature selected from 2008 to 2020 is representative of the status and impact of learning analytics and educational data mining in higher education.

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

Big data and learning analytics are beginning to take root in American higher education institutions as learning and teaching methods change with technologies.

“Big data and learning analytics start taking roots in American HEIs as learning and teaching methods changing with the changing technologies.”

• AssumptionRests on an assumption: The literature selected from 2008 to 2020 is representative of the status and impact of learning analytics and educational data mining in higher education.

May combine two claims.

Required responses to learning analytics are not observed from Russia, China, and India, partly due to lack of awareness.

“due to many factors, one of them is lacking awareness of LA; the required responses are not observed from Russia, China, and India [37].”

• cites [37]Not checked yet

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Classification is generally used in higher education for predicting student performance, behavior, and predicting or preventing students at risk of dropping out, and retention in online courses or e-learning.

“This technique has been used generally in the higher education domain for predicting student performance, behavior, and predicting/preventing students at risk of dropping out, retention in online courses/e-learning environment.”

• AssumptionRests on an assumption: The literature selected from 2008 to 2020 is representative of the status and impact of learning analytics and educational data mining in higher education.

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

Significant influence and coverage of learning analytics have been seen from the USA, UK, Australia, and Canada, but they influence a deficient proportion in China, Korea, Russia, Japan, India, the Philippines, and Malaysia.

“significant influence and coverage of LA have been seen from USA (due to continuous research contribution from the non-profit organization, “EDUCAUSE”), UK (as “JISC', a non-profit organization which provides digital solutions for UK education and skills), Australia, and Canada.However, they can influence a deficient proportion in China, Korea, Russia, Japan, India, the Philippines, and Malaysia.”

• AssumptionRests on an assumption: The literature selected from 2008 to 2020 is representative of the status and impact of learning analytics and educational data mining in higher education.

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

A lot of research outlining the evolution and implementation of learning analytics in Indian higher education is not yet seen.

“A lot of research work that outlines the evolution and implementation of learning analytics in Indian higher education is not seen yet.”

• AssumptionRests on an assumption: The literature selected from 2008 to 2020 is representative of the status and impact of learning analytics and educational data mining in higher education.

May combine two claims.

Classification is the most frequently used LA/EDM technique, followed by clustering, whereas distillation of data for human judgment and discovery with models are rarely applied.

“it is concluded that classification is the most frequently used technique followed by clustering, whereas, a distillation of data for human Judgment, a discovery with models are the techniques which are applied rarely.”

• AssumptionRests on an assumption: The literature selected from 2008 to 2020 is representative of the status and impact of learning analytics and educational data mining in higher education.

May combine two claims.

The review's table analysis indicates that learning analytics and educational data mining methods apply across assessment, feedback, prediction, curriculum, pedagogy, and social learning, together with their learning environments and tools.

“The results of the table show that learning analytics and educational data mining techniques can be used in assessment, feedback, prediction, curriculum, pedagogy, and social learning areas along with their learning environment and techniques/tools used.”

• AssumptionRests on an assumption: The table classification accurately reflects the reviewed literature on learning analytics and educational data mining applications.

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

Some reviewed studies were evaluation studies, while others were empirical studies or surveys.

“Some of them are evaluation studies, while others are empirical studies or surveys.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

The review's tables reveal greater awareness of the topics covered.

“The tables show an increased awareness of the topics of the review.”

Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

Earlier research had not offered a fresh, comprehensive overview of learning analytics and educational data mining in higher education.

“The previously conducted researches were not able to show fresh and novice comprehensive overview of LA/EDM in the higher education domain.”

May combine two claims.

Serious and recurring concerns exist about privacy protection, transparency, data security, ethical boundaries, scalability, and the lack of pedagogy-based strategies.

“There are severe and repeated concerns over privacy protection, transparency, data security, ethical boundaries, data analysis issues like scalability, lack of pedagogy-based strategies.”

• AssumptionRests on an assumption: The challenges identified from the reviewed literature apply to the higher education domain generally.

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

Challenges requiring attention in learning analytics include scalability, privacy, data ownership, and ethics.

“Finally, we present the future directions, with a vision of challenges that need attention, including scalability, privacy, data ownership, and ethics of LA.”

• AssumptionRests on an assumption: The challenges identified from the reviewed literature apply to the higher education domain generally.

May combine two claims.

Classification, clustering, association rule mining, correlation, visualisation, and statistics have been used repeatedly to identify student behavior and motivation, interaction and engagement, course improvement suggestions from feedback, and to help teachers make data-driven curriculum and program quality decisions.

“Techniques such as”

May combine two claims.

Higher education institutions use learning analytics to improve services and resources, to improve learner targets such as grades, pedagogy, emotions, learning behavior, and retention, and to help educators improve teaching.

“HEIs are employing learning analytics to improve the services and resources they provide, to improve visible and non-measurable targets of learners such as grades, pedagogy, emotions, learning behavior, and retention and help educators in improving their teaching practices.”

Classified as probably not a substantial claim. May combine two claims. The quote only partly states this claim.

This review explores social-dimension learning and pedagogy-related issues such as sentiment and emotion analytics using gamification and social learning analysis, which earlier studies treated as a black box.

“Moreover, learning in social dimensions and pedagogy related issues (e.g., sentiment and emotion analytics) are also explored in this study using”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

Learning analytics is a fast-growing platform, and higher education institutions are adapting to it to move beyond conventional analysis and respond to innovations.

“Learning analytics is a fast-growing platform, and HEIs are adapting to it to go beyond the conventional method of analysis and respond to innovative improvements in the area as they take place.”

May combine two claims.

Several techniques are often used to detect unusual student behaviors in learning activities, improve retention and self-regulation, extract student patterns to evaluate and predict outcomes and performance, and support better data-driven decisions to improve education quality.

“Several of these techniques are frequently used to identify and detect students' unusual behaviors in different learning activities to improve their retention rate and self-regulation, to extract the useful student's patterns to evaluate and predict their outcomes and performance and make better data-driven decisions to improve the quality of education.”

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

Learning analytics uses visual data analysis, social network analysis, semantic analysis, and educational data mining to analyze large educational data.

“Figure: [8],shows that LA uses various methods like Visual Data Analysis techniques, Social Network Analysis (SNA), Semantic, and Educational Data Mining (EDM) to analyze the big educational data.”

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• AssumptionRests on an assumption: The table classification accurately reflects the reviewed literature on learning analytics and educational data mining applications.

Probably not a substantial claim: may not pass S4 (based). May combine two claims.

Gamification and social learning analysis are emerging techniques recognized for positive effects on learners and instructors.

“Gamification and Social Learning Analysis are emerging techniques that have been recognized for their positive impact on both learners and instructors.”

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

Virtual learning environments, learning management systems, cognitive tutors, MOOCs, and web-based environments are learning settings used by educational researchers to improve student behavior, self-reflection, self-awareness, assessment, and feedback through learning analytics and educational data mining.

“Virtual Learning Environments (VLEs), Learning Management System (LMS), Cognitive Tutors (CTs), Massive Open Online Courses (MOOCs), Web-based environments are the different learning settings used by educational research community with the objectives to improve students behavior, self-reflection, self-awareness, assessment and feedback services via LA/EDM methods.”

May combine two claims.

The importance of higher education and learning analytics grew rapidly worldwide from 2008 to 2020, driven by the need for data-driven decisions to improve education quality and by growing adoption of advanced technologies.

“It is observed that the higher education and learning analytics significance is multiplying rapidly during the period 2008 to 2020 globally, with the rising need for data-driven decisions for improving the quality of education and the growing adoption of advanced technologies across the higher education sector.”

• AssumptionRests on an assumption: The included journal articles from 2008 to February 2020 are representative of current trends in learning analytics and educational data mining in higher education.

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

Discourse analytics, social network analysis, and sentiment and emotion analytics have developed over many years but were only recently introduced into learning analytics.

“Three techniques that are being applied in learning contexts: discourse analytics, social network analysis, and sentiment and emotion analytics has evolved over numerous years; however, they have recently been introduced into LA.”

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

Finding papers on pedagogy-based approaches is difficult, and few studies empirically justify the effect of analytics-triggered interventions.

“Secondly, it is challenging to find the papers related to pedagogybasedapproaches, and a shortage of studies empirically justifying the effect of analytics triggered interventions.”

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

Both learning analytics and educational data mining seek to improve quality education by improving interventions.

“both LA and EDM aimed at improving quality education by improving interventions [141], [186].”

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Higher education institutions regard learning analytics as a powerful resource that acts as a mirror for them.

“Higher Educational Institutes see Learning Analytics as a powerful resource that worked as a mirror for them.”

Classified as probably not a substantial claim.

Learning analytics assists higher education institutions in many learning and teaching aspects by using data generated about students and their learning backgrounds.

“Learning analytics help Higher Educational Institutes (HEIs) in many aspects of learning and teaching by utilizing the data generated about students and their learning background.”

Probably not a substantial claim: may not pass S4 (based).

Applications of learning analytics boost higher education institutions' ability to improve the educational experience.

“More importantly, the applications of learning analytics give a boost to HEIs to improve the educational experience.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

Advanced technologies and analytical tools are needed to harness the complexity of big data.

“Also, there is a need for advanced technologies and analytical tools to harness the complexity of Big data[187].”

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Data-driven decision approaches are not silver bullets but provide solutions to higher education institutions to some extent.

“Data-driven decision approaches are not silver bullets but provide solutions to higher education institutions to some extent [113].”

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Academic analytics applies analytics methods to satisfy institutional, functional, administrative, and accounting decision-making needs.

“Academic analytics uses analytics methods to meet up the wants of institutional, functional, administration, and accounting decision-making practices [82].”

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The primary goal of both learning analytics and educational data mining is converting raw data into actionable insights that improve education-related decisions.

“While the main aim of both LA and EDM is to turn raw data into actionable insights making education-related decisions better.”

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

Purdue University aimed to boost student success and support students during course learning and final graduation grades by employing learning analytics.

“Purdue University, Indiana, aimed to boost student success and support them during their course learning and so final graduation grades by employing the power of learning analytics [6].”

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Learning analytics creates new schemes and technologies that help higher education institutions and researchers access and genuinely recognize what is happening and how an institution performs overall.

“It creates new schemes and technologies which help HEIs and researchers to gain access and genuinely recognize what is happening, how an institution is performing as a whole [143].”

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Probably not a substantial claim: may not pass S4 (based). May combine two claims.

Insight into education through learning analytics can improve higher education institutions' performance and help them confront academic challenges.

“education insight through LA can improve higher education institutes' performance and help them to face challenges in academia.”

May combine two claims.

Data quality, ownership, access, organizational culture, and available expert knowledge are common issues requiring attention for analytical use of data sets.

“Data quality, ownership, access, organizational culture, and expert knowledge available to employ learning analytics are common questions that needed concentration [11].”

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Visual data mining helps teachers understand how learners pilot in an LMS environment and monitor students easily within a single screen.

“The teachers to understand how the learner's pilot in an LMS environment and monitors the students easily within a single screen [145]”

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Open University uses learning analytics technology to inform teachers on time and provides flourishing interventions that help students attain better outcomes.

“Open University (OU) puts the power of LA technology to informed teachers on time.It provides flourishing interventions that will help the students to attain better outcomes [123].”

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Clustering allows teachers to predict students' learning by analyzing LMS log files to predict dropout at early stages and boost retention rate.

“the clustering technique allows the teachers to predict students learning by analyzing the log files in LMS to predict students drop out at early stages and boost their retention rate [67]”

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Clustering examines how a student interacts in diverse learning circumstances.

“How a student interacts in diverse learning circumstances [143]”

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Sequential pattern represents students' interactions with MOOC course activities to support self-regulated learning.

“Represents students' interactions with the MOOC courses activities to support self-regulated learning [166]”

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

Visual data mining represents high dimensional educational data graphically, making it easy to identify patterns and derive valuable insights.

“Visual data mining technique represents the high dimensional data of the educational domain graphically, so it became easy to identify patterns and derive valuable insights.”

May combine two claims.

Association rule enhances the learning process by predicting student outcomes and identifying which student needed extra concentration from teachers.

“Enhancing the learning process (predict student outcomes and identify which student needed extra concentration from teachers to enhance the overall success of course) based on student's characteristics extracted from logged datasets [9]”

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Association rule is used for designing curriculum, extracting useful student patterns to evaluate and predict outcomes and performance, and making better data-driven decisions to improve quality of education.

“Association rule technique is also used for designing curriculum, for extracting the useful student's patterns to evaluate and predict their outcomes and performance and make better datadriven decisions to improve the quality of education [64], [160].”

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Association rule helps curriculum design, knowing student's area of interest, monitoring, and analysis of academic performance.

“Curriculum design, knowing student's area of interest, monitoring, and analysis of their academic performance [81]”

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The global higher education and learning analytics market is estimated to grow from USD 3.1 billion in 2019 to USD 8.2 billion by 2024, at a compound annual growth rate of 21.5%.

“the global higher education and learning analytics status is estimated to grow from USD 3.1 billion in 2019 to USD 8.2 billion by 2024, at a Compound Annual Growth Rate (CAGR) of 21.5% [177].”

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Statistics data mining helps student retention and success rates.

“Student retention and success rates [32].”

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Clustering helps understand differences and similarities between large data sets for easy decision.

“This technique helps to understand the differences and similarities between the large data sets for an easy decision.”

May combine two claims.

Classification helps instructors improve student learning quality by allowing them to build an effective course curriculum and support learners not performing well in the course.

“The classification technique helps instructors to improve student's learning quality by allowing them to build an effective course curriculum and support learners not performing well in the course [118].”

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Visual data mining supports students' engagement during the course and helps tutors monitor their behavior, contribution, and performance.

“In supporting students' engagement during the course and also helps the tutors to monitors their behavior, contribution and performance [112], [50]”

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Outlier detection helps analyze students' learning difficulty, solve learning problems by identifying irregular learning patterns, and identify deviations in teacher and student behavior or activities during the course.

“which helps in analyzing students learning difficulty, solve students learning problems by identifying irregular learning patterns, identify deviations in the teacher's and student's behavior or activities during the course”

May combine two claims.

Social network analysis is a robust framework for analyses of learner's social behavior, enhancing learners' learning outcomes in online courses, and the relationship between learners and their teachers.

“Based on these, it can be said that SNA is a robust framework for the analyses of learner's social behavior, enhancing learners learning outcomes in online courses, and the relationship between the learners and their teachers.”

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

Text mining is employed in higher education for mining and analyzing big unstructured text datasets to discover patterns, extract useful facts, and support decision making for learners, instructors, and administrators.

“In higher education, the text mining technique is employed for mining and analyzing big unstructured text datasets to discover exciting patterns, extract useful facts, and support decision making for learners, instructors, and administrators.”

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

Implementation of learning analytics in higher education practices diverse goals related to learners, teachers, researchers, administrators and their learning and teaching framework to improve and enhance learning outcomes.

“The implementation of Learning Analytics (LA) in higher education is to practice diverse goals related to various stakeholders (learners, teachers, researchers, administrators) and their learning and teaching framework, to improve and enhance learning outcomes.”

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

LAe-R can automatically assign a grade or score to each criterion related to collaborative interactions and student study behavior in a Moodle course.

“LAe-R can automatically assign grade or score to each criterion related to collaborative interactions and student's study behavior in a Moodle course.”

• cites [35]Not checked yet

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Social network analysis monitors the collaborative learning process and by intervening discovers improvement areas and degree of participation in the course.

“Monitor the collaborative learning process and by intervening to discover improvement areas and their degree of participation in the course [104], [129].”

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Correlation helps instructors prepare course activities or receive feedback regarding the most accessed course materials.

“Help the instructor to prepare course activities or for receiving feedbacks regarding the most accessed course materials [5]”

• cites [5]Not checked yet

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Sequential patterns are used for evaluating students by utilizing their academic performance and grades, leading to better student outcomes.

“It is also used for evaluating students by utilizing their academic performance and grades, which eventually leads to better student outcomes.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

Higher education policymakers can apply text mining techniques to find valuable relationships and patterns by gaining insights into online discussion forums, emails, or chats.

“higher education policymakers can apply text mining techniques to find valuable relationships and patterns by gaining insights into online discussion forums, emails, or chats to support higher education.”

May combine two claims.

Social network analysis uncovers the role of teachers and learners and predicts their performance in the online learning environment.

“Uncover the role of teachers and learners and predict their performance in the online learning environment [72],[130]”

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Sequential pattern helps acquire adequate early warning for students, aiding teachers' teaching plan understanding.

“Acquire adequate early warning for students that helps teachers with their teaching plan understanding [174]”

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Learner engagement, enrollment, and metacognition have been enhanced in the canvas learning management system by implementing formative assessment and regular feedback.

“Learner engagement, enrollment, and metacognition have been enhanced in the canvas learning management system by implementing formative assessment and regular feedback.”

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

The choice of LA/EDM techniques entirely relies on the problem type.

“The choice of techniques entirely relies on the problem type of problem.”

May combine two claims.

Correlation has the power to inspect psychometric characteristics of students and assess students, helping teachers monitor progress in an ongoing course.

“the correlation technique has the power to inspect the psychometric characteristics of the students and also assess students, which helps the teachers to monitors student's progress in an ongoing course [48].”

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Gamification improves cognitive, motivational, and behavioral learning outcomes.

“Improve cognitive, motivational and behavioral learning outcomes [86], [127]”

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Innovative patterns can make feedback equally scalable and useful.

“propose innovative patterns in which feedback is equally scalable and useful.”

• cites [107]Not checked yet

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Pedagogy-based approaches are neglected in learning analytics strategy, while institutions focus only on technical challenges.

“Pedagogy based approaches are ignored as part of the strategy for learning analytics and institutions focused only on addressing technical challenges [91].”

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The DEBE feedback approach informs teachers about their learning theories and reflects whether students found the lecture difficult/easy and boring/engaging.

“a novel feedback approach to inform teachers about their learning theories and advance old ones in order to reflect whether the students found the lecture to be Difficult/Easy and Boring/Engaging.”

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The framework can be applied in both offline and online environments.

“The proposed framework can be applied in both offline and online environments.”

• cites [171]Not checked yet

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A mathematical model in Moodle LMS for peer assessment using learning analytics uses scoring rubrics for quality assessment, overcoming past validity and reliability issues.

“The authors in [36] implemented a mathematical model in Moodle LMS (Learning Management System) for peer assessment using learning analytics.They use scoring rubrics for quality assessment, which overcomes the problem of past researches, i.e., validity and reliability issues.”

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LAe-R helps teachers assess individual student performance and the average performance of all students in the course.

“It helps the teacher to assess individual student's performance and the average performance of all the students in the course.”

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

Current assessment in computer-supported collaborative learning is time-consuming and does not support reliability, validity, and individual accountability issues.

“the current assessment in CSCL (Computer-Supported Collaborative Learning) is time-consuming and does not support reliability, validity, and individual accountability issues.”

• cites [169]Not checked yet

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Learning analytics in a digital learning environment supports less adaptive learners.

“In work by [152] implemented LA in a digital learning environment to support less adaptive learners.”

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Challenges of feedback in higher education fall into three categories: feedback practices, contextual constraints, and individual capacity.

“They noticed three categories of challenges: 1) feedback practices (connected with the communication of feedback comments), 2) contextual constraints (related with institutional constraints like time and scalability of feedback practices) and 3) individual capacity (related with attitudes and capabilities of students and staff) that students and staff experienced.”

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A learning analytics framework can forecast student success by assessing learning activities in the Moodle LMS environment.

“the authors in [171]proposed a learning analytics framework to forecast student's success by assessing the learning activities in the Moodle LMS environment.”

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This systematic literature review contributes an investment in learning analytics and educational data mining in higher education worldwide that will enhance outcomes for students, teachers, institutions, and society.

“This systematic literature review provides an investment in LA/EDM by the higher education domain around the world that will direct to enhance performance and outcomes for students, teachers, HEIs, and immense society.”

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

A clustering algorithm through the lens of learning analytics automatically assesses student performance in an online learning environment to support both learners and teachers.

“They proposed a clustering algorithm through the lens of learning analytics that automatically assesses student's performance in an online learning environment to support both learners and teachers.”

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LOCO-Analyst explains different tools and aids learners are turning to and can offer different types of feedback to teachers/tutors to improve the learning process.

“the proposed approach also explains different tools and aids learners are turning to and can offer different types of feedback to teachers/tutors to improve the learning process.”

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Dispositional Learning Analytics predicts students at risk and provides actionable feedback to both students and teachers.

“Dispositional Learning Analytics (DLA) not only predict students at risk but also provide both students and teachers with actionable feedback.”

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Adding a 'student's learning dispositions' source makes feedback more actionable to improve the learning of underperforming students.

“The proposed framework adds a 'student's learning dispositions' source to make feedback more actionable in order to improve the learning of underperforming students.”

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LOCO-Analyst generates and promotes feedback for teachers.

“The authors implemented a novel tool in [70] for generating and promoting feedback for teachers popularly known as LOCO-Analyst.”

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The proposed algorithm assesses strengths and weaknesses in individual student's involvement in collaborative activities, accelerating the assessment method.

“The proposed algorithm assesses strengths and weaknesses in individual student's involvement in collaborative activities, thus accelerate the assessment method.”

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A framework based on assessment theory and related feedback concepts supports self-regulated learning processes through the lens of learning analytics.

“The authors in [135] derived a framework based on the assessment theory and related feedback concepts to support self-regulated learning processes through the lens of LA.”

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DLA analyzes cognitive, metacognitive, engagement, emotions, and motivation factors of students, helps provide actionable feedback, and design effective interventions.

“the proposed approach also analyze the cognitive, metacognitive, engagement, emotions, and motivation factors of students, help in providing actionable feedback, and designing effective interventions.”

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Formative feedback methodology using learning analytics can overcome missing elements like student retention and course success rate.

“explores the advantage of formative feedback methodology using learning analytics to overcome the missing elements like student retention and course success rate.”

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Many indicators have been polished relative to the skill to work collaboratively.

“Furthermore, many indicators have been polished relative to the skill to work collaboratively.”

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  Not checked in the free run.

Classified as probably not a substantial claim.

Student activities can be used as indicators for instructors to provide feedback to large student groups through personalized messages.

“student's activities are used as indicators for instructors to provide feedback tolarge student groups through personalized messages.”

• cites [106]Not checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S2 (checkable).

Table 2 can help higher education institutions choose the right technique for the right use.

“Table 2 in this study can support HEIs in determining the exact technique for the right use.”

Classified as probably not a substantial claim.

Applying and implementing learning analytics and educational data mining can bring significant advantages to learners and educators.

“The discussion based on the application and implementation of LA/EDM can present significant advantages to both learners and educators, and therefore recommend HEIs to adopt them where sufficient.”

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

Papers from the International Conferences on Learning Analytics in Asia and some other popular conferences were excluded because they were scheduled after the review process.

“Lastly, in this study, the papers presented at the International conferences on Learning Analytics in Asia and some other popular conferences which have also done work in the LA field have been excluded from the review process, as those are scheduled to take place after the review process.”

• AssumptionRests on an assumption: Excluding conference papers scheduled after the review process does not materially bias the review's conclusions.

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

The review included recently published journal articles from 2008 to February 2020, even with fewer citations, to show current trends.

“However, it is decided to include recently published journal articles (published from 2008 to Feb 2020) even with fewer citations to indicate the current trends in this domain.”

• AssumptionRests on an assumption: The included journal articles from 2008 to February 2020 are representative of current trends in learning analytics and educational data mining in higher education.

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

Interventions are an essential part of the learning analytics and educational data mining process because raw data becomes actionable intelligence at this step.

“Interventions are an essential measurement of the learning analytics and educational data mining process, as in this step, raw data is turned into actionable intelligence [105]”

• cites [105]Not checked yet

  Not checked in the free run.

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

Pedagogical interventions can achieve these objectives.

“These objectives can be attained through pedagogical interventions.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

Reviewed work from individual researchers, global joint efforts, and organizations such as SoLAR, ERIC, EDUCAUSE, and LAK can collectively influence research directions in learning analytics.

“The Quality of work reviewed from individual researchers to global joint efforts and well-known research organizations (such as SoLAR, ERIC, EDUCAUSE, and LAK) can collectively present an incredible influence to have a significant impact on the type of researches to empower new directions in the learning analytics field.”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

The technique chosen generally depends on the nature or category of the problem to be solved.

“The choice of which technique to employ or apply generally depends on the nature/category of the problem to be resolved.”

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

This study did not examine the actual use of theories or real-time application of some technologies.

“First, this study does not explore the actual utilization of theories and real-time application of some technologies.”

Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

Conclusions from these processes can enable higher education institutions to take needed measures and actions appropriately and promptly.

“The conclusions from these processes can empower the HEIs to take the required measures and actions in an appropriate and timely manner.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

Low completion rates in MOOCs are often a problem in learning analytics, caused by lack of motivation, metacognition, self-regulated learning techniques, vocational skills, and criterion-based assessment.

“It is observed that the low completion rate of MOOCs (e.g., Coursera, EdX, Udacity, and FutureLearn) is often a problem in the learning analytics field.This is because there is a lack of motivation, metacognition, self-regulated learning techniques, vocational skills, and criterion-based assessment, so attention should be required in this area, which otherwise leads to the failure of MOOCs.”

• AssumptionRests on an assumption: The included journal articles from 2008 to February 2020 are representative of current trends in learning analytics and educational data mining in higher education.

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

Scalability is a crucial issue in learning analytics because learner data is growing rapidly due to the digital revolution.

“Scalability is a crucial issue in the LA field as the learner's data is growing at a swift pace due to the digital revolution.”