From Study Tactics to Learning Strategies: An Analytical Method for Extracting Interpretable Representations

Fincham, Ed; Gašević, Dragan; Jovanović, Jelena; Pardo, Abelardo

doi:10.1109/tlt.2018.2823317

Cited by 99 publications

(133 citation statements)

References 51 publications

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“…In particular, research shows that online teachers make use of multiple non-PLA-related data sources to inform their understanding of whether and how students engage with online courses (Fincham et al, 2018;Rienties et al, 2018). Accordingly, teachers at The Open University, UK (OU) provide support using available student's data (demographics, VLE engagement, etc), student's contact history, made available via the so-called student support teams (SSTs), who get in touch with students, as well as a system that monitors the (non) submission of assessments.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…While an emerging body of learning analytics literature has highlighted mixed results about the effectiveness of PLA for actionable interventions and proactive support (Ferguson & Clow, 2017;Fincham, Gasevic, Jovanovic, & Pardo, 2018;Viberg, Hatakka, Bälter, & Mavroudi, 2018), it is yet unanswered whether and how the PLA can effectively empower teachers to intervene on time, on a large-scale basis and across a range of disciplinary contexts. As evidenced in several recent reviews about the uptake of learning analytics and PLA in particular (Ferguson et al, 2016;Ferguson & Clow, 2017;Viberg et al, 2018), although institutions and teachers are interested in learning analytics, their actual uptake in most institutions is rather limited.…”

mentioning

confidence: 99%

“…As evidenced in several recent reviews about the uptake of learning analytics and PLA in particular (Ferguson et al, 2016;Ferguson & Clow, 2017;Viberg et al, 2018), although institutions and teachers are interested in learning analytics, their actual uptake in most institutions is rather limited. Also, there is limited evidence beyond relatively small case studies of PLA usage with teachers (van Leeuwen et al, 2014), experimental lab studies (Worsley & Blikstein, 2015) and MOOC contexts (Kizilcec, Pérez-Sanagustín, & Maldonado, 2017) that PLA can have sustained positive effects on learners and learning outcomes (Fincham et al, 2018).…”

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confidence: 99%

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Empowering online teachers through predictive learning analytics

Herodotou¹,

Hlosta²,

Boroowa³

et al. 2019

Brit J Educational Tech

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This study presents an advanced predictive learning analytics system, OU Analyse (OUA), and evidence from its evaluation with online teachers at a distance learning university. OUA is a predictive system that uses machine learning methods for the early identification of students at risk of not submitting (or failing) their next assignment. Teachers have access, via interactive dashboards, to weekly predictions of risk of failing for each of their students. In this study, we examined how the degree of OUA usage by 559 teachers, of which 189 were given access to OUA, related to student learning outcomes of more than 14 000 students in 15 undergraduate courses. Teachers who made “average” use of OUA, that is accessed OUA throughout the life cycle of a course presentation, and in particular between 10% and 40% of the weeks a course was running, and intervened with students flagged as at risk were found to benefit their students the most; after controlling for differences in academic performance, these students were found to have significantly better performance than their peers in the previous year's course presentation during which the same teachers made no use of predictive learning analytics. Predictive learning analytics is an innovative student's support approach in online pedagogy that, as shown in this study, can empower online teachers in effectively monitoring and intervening with their students, over and above other approaches, and result in improved learning outcomes. What is already known about this topic Pedagogical and personal support to students is a significant responsibility of online teachers. Student's support is a challenging activity due to the lack of face‐to‐face interactions. Predictive learning analytics (PLA) can identify students at risk of failing their studies. What this paper adds One of the few large‐scale studies is available for examining the impact of analytics on student's performance. Teachers' usage of PLA was significantly related to better learning outcomes. Online teachers had students with better learning outcomes when accessing PLA data rather than when they had no access. Implications for practice and/or policy PLA can empower online teachers and complement the teaching practice. PLA can help in the identification and proactive intervention of students at risk of failing their studies. Actions should be taken to motivate and engage online teachers with PLA.

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Section: Theoretical Backgroundmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Empowering online teachers through predictive learning analytics

Herodotou¹,

Hlosta²,

Boroowa³

et al. 2019

Brit J Educational Tech

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“…As outlined above, trace data allow for the capturing of learners' activities unobtrusively over time at a very fine granularity. Trace data provide a reliable measure of when students engaged in the online learning environment and with what materials they engaged, and offers information about temporal and sequential characteristics of activities, making it a readily available and valuable data source to study learners' SRL (Cicchinelli et al, 2018;Fincham, Gasevic, Jovanovic, & Pardo, 2018;Hadwin et al, 2007;Kizilcec, Pérez-Sanagustín, & Maldonado, 2017;Maldonado-Mahauad, Pérez-Sanagustín, Kizilcec, Morales, & Munoz-Gama, 2018;Winne, 2014). Several authors have used trace data to attempt to locate learner behaviour that is representative of SRL activities (Van Laer & Elen, 2018).…”

Section: Measuring Srl With Trace Data: Affordances and Challengesmentioning

confidence: 99%

A mixed method approach to studying self-regulated learning in MOOCs

Jansen

Leeuwen

Janssen

et al. 2020

FLR

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To be successful in online education, learners should be able to self-regulate their learning due to the autonomy offered to them. Accurate measurement of learners' selfregulated learning (SRL) in online education is necessary to determine which learners are in need of support and how to best offer support. Trace data is gathered automatically and unobtrusively during online education, and is therefore considered a valuable source to measure learners' SRL. However, measuring SRL with trace data is challenging for two main reasons. First, without information on the how and why of learner behaviour it is difficult to interpret trace data correctly. Second, SRL activities outside of the online learning environment are not captured in trace data. To address these two challenges, we propose a mixed method approach with a sequential design. Such an approach is novel for the measurement of SRL. We present a pilot study in which we combined trace data with interview data to analyse learners' SRL in online courses. In the interview, cued retrospective reporting was conducted by presenting learners with visualizations of their trace data. In the second part of the interview, learners' activities outside of the online course environment were discussed. The results show that the mixed-method approach is indeed a promising approach to address the two described challenges. Suggestions for future research are provided, and include methodological considerations such as how to best visualize trace data for cued retrospective recall.

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“…The learning situations are built on these conditions, indicating that learner approaches and decision-making processes are personalised by prior individual and group experiences over time and events (Hadwin, Järvelä, & Miller, 2017). Even though 'objective' process methods, (e.g., video records or log traces that accumulate from technology-based learning environments) can provide rich accounts of learners' and groups' actions and visible reactions in the moment, understanding reasons behind these actions require 'subjective data' (e.g., questionnaires, diaries, interviews) (Berger & Karabenick, 2016;Fincham et al, 2018;Järvelä, Malmberg, Sobocinski, Haataja, & Kirschner, 2018;Malmberg et al, 2013).…”

Section: Claim: Motivation and Emotion Regulation Is Influenced By Thmentioning

confidence: 99%

Capturing motivation and emotion regulation during a learning process

Järvenoja

Järvelä

Törmänen

et al. 2018

FLR

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This paper describes our research approach in which we have focused on situational and contextual variations in motivation and emotion regulation to better understand its role, appearance and function in collaborative learning situations. We have used research designs that employ process-oriented measures combined with subjective interpretations to capture motivation and emotion regulation. Analysing on-line process data poses several challenges such as variation in the granularity of different data sources, problems that emerge due to the complexity of contextual and situational factors in ecologically-valid learning situations or, currently, challenges in the use of multiple data channels and their analyses.In this paper, we present three claims underlying our research, particularly the motivation and emotions and their regulation in learning. The claims are as follows: (1) motivation and emotion regulation is situation and context specific, (2) motivation and emotion regulation is influenced by multi-layered nature of motivation and (3) Motivation and emotion regulation is intertwined with other processes of learning and can be captured from their temporal manifestation. We present an example from our empirical study to discuss how these claims have led us to employ multiple process-oriented methods that include both subjective and objective data sources, including different combinations of situation-specific self-reports, video and physiological data. We then describe opportunities and challenges involved in the empirical studies.

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From Study Tactics to Learning Strategies: An Analytical Method for Extracting Interpretable Representations

Cited by 99 publications

References 51 publications

Empowering online teachers through predictive learning analytics

Empowering online teachers through predictive learning analytics

A mixed method approach to studying self-regulated learning in MOOCs

Capturing motivation and emotion regulation during a learning process

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