Quantum of Choice: How learners’ feedback monitoring decisions, goals and self-regulated learning skills are related

Jivet, Ioana; Wong, Jacqueline; Scheffel, Maren; Torre, Manuel Valle; Specht, Marcus; Drachsler, Hendrik

doi:10.1145/3448139.3448179

Cited by 35 publications

(27 citation statements)

References 38 publications

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“…Further investigations into the details of the log data from Moodle revealed that these engagement behaviours mainly focused on sharing individual reflections and asking questions to teachers. This particularly links to the recent findings from Jivet et al [35] that only the high SRL skills, specifically high help-seeking skills will value the discussion forum as a sort of help. It was also observed that students with high SRL competence have a higher frequency of viewing others' reflection input as well as viewing discussions between students and teachers.…”

Section: Discussionsupporting

confidence: 65%

Different modality, different design, different results: Exploring self-regulated learner clusters’ engagement behaviours at individual, group and cohort activities

Zhou¹,

Suraworachet²,

Cukurova³

2021

Preprint

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Self-Regulated Learning (SRL) competence is an important aspect of online learning. SRL is an internal process, but analytics can offer an externalisation trigger to allow for observable effects on learner behaviours. The purpose of this paper is to explore the relationship between students’ SRL competence and their learning engagement behaviours observed in multimodal data. In a postgraduate course with 42 students, eighteen features from three types of data in seven learning activities were extracted to investigate multi-level SRL competence students’ engagement behaviours. The results revealed that students with different SRL competence clusters might exhibit different behaviours in individual, group, and cohort level learning activities. Also, students with similar SRL competence might exhibit significantly different engagement behaviours in different learning activities, depending on the learning design. Therefore, while using engagement data in AIED systems; the modality of the data, specific analysis techniques used to process it, and the contextual particularities of the learning design should all be explicitly presented. So that, they can be considered in the interpretations of automated decisions about student achievement.

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Section: Discussionsupporting

confidence: 65%

Different modality, different design, different results: Exploring self-regulated learner clusters’ engagement behaviours at individual, group and cohort activities

Zhou¹,

Suraworachet²,

Cukurova³

2021

Preprint

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“…We conducted comprehensive searches on Scopus (390 documents) and Web of Science (164 documents) in May 2022. Additionally, we used Google Scholar to search for records published by the Journal of Learning Analytics (142), the Journal of Educational Data Mining (35), and the International Conference on Educational Data Mining (22). The Journal of Learning Analytics, Journal of Educational Data Mining, and International Conference of Educational Data Mining are not fully indexed in the Scopus or Web of Science databases.…”

Section: Methodsmentioning

confidence: 99%

Supporting self-regulated learning with learning analytics interventions – a systematic literature review

Heikkinen

Saqr

Malmberg

et al. 2022

Preprint

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During the past years scholars have shown an increasing interest in supporting students' self-regulated learning (SRL). Learning analytics (LA) can be applied in various ways to identify a learner’s current state of self-regulation and support SRL processes. It is important to examine how LA has been used to identify the need for support in different phases of SRL cycle, which channels are used to mediate the intervention and how efficient and impactful the intervention is. This will help the learners to achieve the anticipated learning outcomes. The systematic literature review followed PRISMA 2020 statement to examine studies that applied LA interventions to enhance SRL. The search terms used for this research identified 753 papers in May 2021. Of these, 56 studies included the elements of LA, SRL, and intervention. The reviewed studies contained various LA interventions aimed at supporting SRL, but only 46% of them revealed a positive impact of an intervention on learning. Furthermore, only four studies reported positive effects for SRL and covered all three SRL phases (planning, performance, and reflection). Based on the findings of this literature review, the key recommendation is for all phases of SRL to be considered when planning interventions to support learning. In addition, more comparative research on this topic is needed to identify the most effective interventions and to provide further evidence on the effectiveness of interventions supporting SRL.

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“…Examples of such product data are the outcomes of formative assessments or diagnostic entry tests. In applications of DLA, a third data source is provided by the self-report surveys applied to measure learning dispositions; although attempts are being made to measure dispositions through the observation of learning behaviors (Buckingham Shum and Deakin Crick, 2016;Cloude et al, 2020;Jivet et al, 2021;Salehian Kia et al, 2021), the survey method is still dominant (Shum and Crick, 2012).…”

Section: Personalized Learning and Multi-modal Data Sourcesmentioning

confidence: 99%

“…In Dispositional Learning Analytics (DLA) researchers aim to complement trace data with other subjective (e.g., survey data) and/or objective (e.g., continuous engagement proxies) measures of learners' orientation to learning. Recently several attempts are being made to identify behavioral proxies of learning dispositions that are trace-based (Connected Intelligence Centre, 2015;Buckingham Shum and Deakin Crick, 2016;Jivet et al, 2021;Salehian Kia et al, 2021). For example, in a study of 401 MOOC learners, Jivet et al (2021) allowed participants to select a learning analytics dashboard that matched with their respective phase of self-regulated learning (SRL).…”

Section: Introductionmentioning

confidence: 99%

“…Recently several attempts are being made to identify behavioral proxies of learning dispositions that are trace-based (Connected Intelligence Centre, 2015;Buckingham Shum and Deakin Crick, 2016;Jivet et al, 2021;Salehian Kia et al, 2021). For example, in a study of 401 MOOC learners, Jivet et al (2021) allowed participants to select a learning analytics dashboard that matched with their respective phase of self-regulated learning (SRL). The findings indicated that learners overwhelmingly chose indicators about completed activities.…”

Section: Introductionmentioning

confidence: 99%

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Dispositional Learning Analytics for Supporting Individualized Learning Feedback

2021

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An important goal of learning analytics (LA) is to improve learning by providing students with meaningful feedback. Feedback is often generated by prediction models of student success using data about students and their learning processes based on digital traces of learning activities. However, early in the learning process, when feedback is most fruitful, trace-data-based prediction models often have limited information about the initial ability of students, making it difficult to produce accurate prediction and personalized feedback to individual students. Furthermore, feedback generated from trace data without appropriate consideration of learners’ dispositions might hamper effective interventions. By providing an example of the role of learning dispositions in an LA application directed at predictive modeling in an introductory mathematics and statistics module, we make a plea for applying dispositional learning analytics (DLA) to make LA precise and actionable. DLA combines learning data with learners’ disposition data measured through for example self-report surveys. The advantage of DLA is twofold: first, to improve the accuracy of early predictions; and second, to link LA predictions with meaningful learning interventions that focus on addressing less developed learning dispositions. Dispositions in our DLA example include students’ mindsets, operationalized as entity and incremental theories of intelligence, and corresponding effort beliefs. These dispositions were inputs for a cluster analysis generating different learning profiles. These profiles were compared for other dispositions and module performance. The finding of profile differences suggests that the inclusion of disposition data and mindset data, in particular, adds predictive power to LA applications.

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Quantum of Choice: How learners’ feedback monitoring decisions, goals and self-regulated learning skills are related

Cited by 35 publications

References 38 publications

Different modality, different design, different results: Exploring self-regulated learner clusters’ engagement behaviours at individual, group and cohort activities

Different modality, different design, different results: Exploring self-regulated learner clusters’ engagement behaviours at individual, group and cohort activities

Supporting self-regulated learning with learning analytics interventions – a systematic literature review

Dispositional Learning Analytics for Supporting Individualized Learning Feedback

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