A rule-based indicator definition tool for personalized learning analytics

Muslim, Arham; Chatti, Mohamed Amine; Mahapatra, Tanmaya; Schroeder, Ulrik

doi:10.1145/2883851.2883921

Cited by 16 publications

(8 citation statements)

References 3 publications

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“…An analytics dashboard designed for users of interactive e-books could potentially be used by teachers [41]. A rule-based indicator definition tool (RIDT) could support a personalized learning analytics experience (N=5 staff, 7 students) [47]. Studying eye fixation patterns could enable educators to understand how their instructional design using online learning environments can stimulate higher-order cognitive activities (N=60).…”

Section: Evidence With Potential To Support Teachingmentioning

confidence: 99%

Where is the evidence?

Ferguson

Clow

2017

Proceedings of the Seventh International Learning Analytics &Amp; Knowledge Conference

105

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Where is the evidence for learning analytics? In particular, where is the evidence that it improves learning in practice? Can we rely on it? Currently, there are vigorous debates about the quality of research evidence in medicine and psychology, with particular issues around statistical good practice, the 'file drawer effect', and ways in which incentives for stakeholders in the research process reward the quantity of research produced rather than the quality. In this paper, we present the Learning Analytics Community Exchange (LACE) project's Evidence Hub, an effort to relate research evidence in learning analytics to four propositions about learning analytics: whether they support learning, support teaching, are deployed widely, and are used ethically. Surprisingly little evidence in this strong, specific sense was found, and very little was negative (7%, N=123), suggesting that learning analytics is not immune from the pressures in other areas. We explore the evidence in one particular area in detail (whether learning analytics improve teaching and learners support in the university sector), and set out some of the weaknesses of the evidence available. We conclude that there is considerable scope for improving the evidence base for learning analytics, and set out some suggestions of ways for various stakeholders to achieve this.

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Section: Evidence With Potential To Support Teachingmentioning

confidence: 99%

Where is the evidence?

Ferguson

Clow

2017

Proceedings of the Seventh International Learning Analytics &Amp; Knowledge Conference

105

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“…Most of these instructors had used TrACE prior to this analysis, so they may have already formed habits. This was a key difference from many other studies on system use, which were tested with first-time users of LA [2,12,1]. The largest class had 59 students.…”

Section: Methodsmentioning

confidence: 97%

Examining Instructor Use of Learning Analytics

Dazo

Stepanek

Chauhan

et al. 2017

Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems

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This study takes an instructor-centric approach to Learning Analytic (LA) research by analyzing instructor use of the LA within an educational streaming video platform called TrACE. The goal of this study is to understand how instructors naturally interact with analytic dashboards through an empirical analysis. To accomplish this, data of 14 instructors from three institutions that used TrACE from Spring 2015 to Spring 2016 was collected. Data was analyzed to identify frequency of analytic visits, duration of analytic use, differences in analytic use, and differences in use between semesters. Instructors demonstrated preferences for some analytics over others, but the majority of teachers generate short sessions that may not allow for in-depth exploration in analytics. Finally, instructor activity is not always consistent between semesters. Focus groups were conducted to explore motivations behind these findings and future work includes developing LA that address discovered issues.

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“…Several works and surveys have identified the availability of tools providing teachers customization capabilities as a must for the adoption of LA [8], [9], [11], [12], [29]. Instead of predetermined analysis and visualization, such tools should allow teachers to configure the stages of a LA workflow.…”

Section: A La Customization Approachesmentioning

confidence: 99%

“…Providing teachers with customization capabilities have been pointed out as a must for the adoption of LA [8], and some proposals have recently been presented [9]- [12]. In addition to a predefined analysis and visualization, LA tools should allow teachers to personalize these LA workflow stages.…”

Section: Introductionmentioning

confidence: 99%

“…Firstly, a textual DSL was created that enables teachers to customize the LA dashboards. Other works have recently presented proposal to customize LA solutions, but the mechanisms provided to teachers are more limited, as discussed in detail in Section VI: wizards to choose predefined indicators [10], [12], [16] or DSLs that offer a more simple expression language to define indicators [11], [17], or DSLs targeted to developers instead of teachers [18].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Customizable and Incremental Processing Approach for Learning Analytics

2020

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The ability of learning analytics to improve the learning/teaching processes is widely recognized. In this paper, the learning analytics architecture developed at the Digital Content Production Center of the Technical University of Cartagena (Spain) is presented. This architecture contributes to the field of learning analytics in two aspects: it allows for dashboard customization and improves the efficiency of the analysis of learners' interaction data. Events resulting from learners' interaction are captured and stored in Caliper standard format, to be further processed incrementally to allow dashboards to be shown without delay to teachers. Customization is considered a mandatory requirement for learning analytics tools, however, although some proposals have recently been made, a greater research effort in this topic is necessary. In the present work, this requirement is addressed by defining a domain-specific language (DSL) that allows teachers to customize dashboards. This language allows to express indicators (logical expressions) that classify students into different groups depending on their performance level. The paper also shows how our learning analytics approach was evaluated with a course that applies a flipped classroom method, and how it compares to the most relevant related works that have been published.

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A rule-based indicator definition tool for personalized learning analytics

Cited by 16 publications

References 3 publications

Where is the evidence?

Where is the evidence?

Examining Instructor Use of Learning Analytics

A Customizable and Incremental Processing Approach for Learning Analytics

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