Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours

Nguyen, Quan; Huptych, Michal; Rienties, Bart

doi:10.18608/jla.2018.53.8

Cited by 25 publications

(29 citation statements)

References 45 publications

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“…( 2016 ) proposed that a positive student experience of self-efficacy, tests, motivation, self-regulation and positive interaction with many online events, particularly those that offer feedback, reflection and reasoning, would correlate with academic achievement. In another study to investigate students' engagement in an online course, Nguyen et al . ( 2018 ) found, that high-performing students were studying in advance, while low-performing students showed more catching-up activities.…”

Section: Engagementmentioning

confidence: 99%

“…( 2018 ) found, that high-performing students were studying in advance, while low-performing students showed more catching-up activities. For example, Nguyen et al . ( 2018 ) showed that for certain lessons in the later parts of the course all the students spent significantly more time on learning.…”

Section: Engagementmentioning

confidence: 99%

“…However, the passing students spent more time in learning in advance while failed students spent more time catching-up with the material. Therefore, the results from both studies implicate that linking LD with MMLA-based insights, such as pointing out students to certain materials (eg, relevant online events: Pardo et al ., 2016 ) or frontloading and scaffolding learning materials that cause engagement and were avoided; and the ones causing disengagements (Nguyen et al ., 2018 ). Both studies also call for further research actions using MMD-based behavioral patterns to uncover student disengagement and to highlight the potential of MMD-based insights to inform the LD and to evaluate the implications of this for engagement (Nguyen et al ., 2018 ;Pardo et al ., 2016 ).…”

Section: Engagementmentioning

confidence: 99%

“…( 2015 ) 206 Voice, grades Cukurova et al . ( 2018 ) 45, 36 Video data, log data Nguyen et al . ( 2018 ) 182 Clicks, views Liu et al .…”

Section: Appendix Amentioning

confidence: 99%

“…The confluence of multimodal data (MMD) with advanced computational analyses (multimodal learning analytics-MMLA, as the literature refers to them) enables us to understand and support complex learning phenomena (Blikstein & Worsley, 2016 ). For example, eye-tracking data and the different linguistic and prosodic features of speech can inform us about the students' expertise (Andrade, Delandshere, & Danish, 2016 ;Mangaroska, Vesin, & Giannakos, 2019 ); or video data can tell us about their engagement (Nguyen, Huptych, & Rienties, 2018 ;Pardo, Han, & Ellis, 2016 ). These insights can enable actionable feedback to be provided to the learners.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Multimodal data capabilities for learning: What can multimodal data tell us about learning?

Sharma¹,

Giannakos²

2020

Brit J Educational Tech

119

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Most research on learning technology uses clickstreams and questionnaires as their primary source of quantitative data. This study presents the outcomes of a systematic literature review of empirical evidence on the capabilities of multimodal data (MMD) for human learning. This paper provides an overview of what and how MMD have been used to inform learning and in what contexts. A search resulted in 42 papers that were included in the analysis. The results of the review depict the capabilities of MMD for learning and the ongoing advances and implications that emerge from the employment of MMD to capture and improve learning. In particular, we identified the six main objectives (ie, behavioral trajectories, learning outcome, learning‐task performance, teacher support, engagement and student feedback) that the MMLA research has been focusing on. We also summarize the implications derived from the reviewed articles and frame them within six thematic areas. Finally, this review stresses that future research should consider developing a framework that would enable MMD capacities to be aligned with the research and learning design (LD). These MMD capacities could also be utilized on furthering theory and practice. Our findings set a baseline to support the adoption and democratization of MMD within future learning technology research and development. What is already known about this topic Capturing and measuring learners’ engagement and behavior using MMD has been explored in recent years and exhibits great potential. There are documented challenges and opportunities associated with capturing, processing, analyzing and interpreting MMD to support human learning. MMD can provide insights into predicting learning engagement and performance as well as into supporting the process. What this paper adds Provides a systematic literature review (SLR) of empirical evidence on MMD for human learning. Summarizes the insights MMD can give us about the learning outcomes and process. Identifies challenges and opportunities of MMD to support human learning. Implications for practice and/or policy Learning analytics researchers will be able to use the SLR as a guide for future research. Learning analytics practitioners will be able to use the SLR as a summary of the current state of the field.

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Section: Engagementmentioning

confidence: 99%

Section: Engagementmentioning

confidence: 99%

Section: Engagementmentioning

confidence: 99%

“…( 2015 ) 206 Voice, grades Cukurova et al . ( 2018 ) 45, 36 Video data, log data Nguyen et al . ( 2018 ) 182 Clicks, views Liu et al .…”

Section: Appendix Amentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Multimodal data capabilities for learning: What can multimodal data tell us about learning?

Sharma¹,

Giannakos²

2020

Brit J Educational Tech

119

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Individual differences in the preference for worked examples: Lessons from an application of dispositional learning analytics

Tempelaar

Rienties

Nguyen

2020

Applied Cognitive Psychology

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Summary Worked‐examples have been established as an effective instructional format in problem‐solving practices. However, less is known about variations in the use of worked examples across individuals at different stages in their learning process in student‐centred learning contexts. This study investigates different profiles of students' learning behaviours based on clustering learning dispositions, prior knowledge, and the choice of feedback strategies in a naturalistic setting. The study was conducted on 1,072 students over an 8‐week long introductory mathematics course in a blended instructional format. While practising exercises in a digital learning environment, students can opt for tutored problem solving, untutored problem solving, or call worked examples. The results indicated six distinct profiles of learners regarding their feedback preferences in different learning phases. Finally, we investigated antecedents and consequences of these profiles and investigated the adequacy of used feedback strategies concerning ‘help‐abuse’. This research indicates that the use of instructional scaffolds as worked‐examples or hints and the efficiency of that use differs from student to student, making the attempt to find patterns at an overall level a hazardous endeavour.

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Improving MOOC learning performance in China: An analysis of factors from the TAM and TPB

Wang

Dong

Zhang

2020

Comp Applic In Engineering

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Recent years have seen a rapid development in the integration of information technology into China's higher education provision. Massive open online courses (MOOCs) have played a vital role in spreading and sharing educational content in China, but problems have been identified with sustaining the learning process, leading to low completion rates. This study integrates the technology acceptance model and the theory of planned behavior to build a theoretical model of the mechanism behind MOOC learning performance. Data were collected from an online questionnaire (N = 638) completed by participants in MOOCs offered by Anhui Agricultural University. Structural equation modeling was then used to analyze the participants' self-reported MOOC learning experience and outcomes and verify the hypothesized path to improving MOOC learning performance. The results indicate that perceived usefulness, learning attitudes, subjective norms, perceived behavioral control, and learning behavior had significant effects on learning performance, while perceived ease of use was not a significant factor.

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Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours

Cited by 25 publications

References 45 publications

Multimodal data capabilities for learning: What can multimodal data tell us about learning?

Multimodal data capabilities for learning: What can multimodal data tell us about learning?

Individual differences in the preference for worked examples: Lessons from an application of dispositional learning analytics

Improving MOOC learning performance in China: An analysis of factors from the TAM and TPB

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