Investigating the effect of an adaptive learning intervention on students’ learning

Liu, Min; McKelroy, Emily; Corliss, Stephanie B.; Carrigan, Jamison E.

doi:10.1007/s11423-017-9542-1

Cited by 99 publications

(65 citation statements)

References 29 publications

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“…Self-assessment leads students to a greater self-awareness, by facilitating self-regulation of motivation and actions (McMillan & Hearn, 2008), and inherently promotes students' effortful behavior, because the result of the assessment is primarily important to the student herself (Papamitsiou & Economides, 2019). Furthermore, adaptation has shown a positive impact on learners' engagement with the activities (Normadhi et al, 2019), which is also reflected on learners' performance (Barla et al, 2010;Liu, McKelroy, Corliss, & Carrigan, 2017). In addition, previous work with multimodal data yielded results in other learning settings.…”

Section: Motivation Of the Research And Research Questionmentioning

confidence: 99%

Building pipelines for educational data using AI and multimodal analytics: A “grey‐box” approach

Sharma¹,

Papamitsiou²,

Giannakos³

2019

Brit J Educational Tech

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Students' on‐task engagement during adaptive learning activities has a significant effect on their performance, and at the same time, how these activities influence students' behavior is reflected in their effort exertion. Capturing and explaining effortful (or effortless) behavior and aligning it with learning performance within contemporary adaptive learning environments, holds the promise to timely provide proactive and actionable feedback to students. Using sophisticated machine learning (ML) algorithms and rich learner data, facilitates inference‐making about several behavioral aspects (including effortful behavior) and about predicting learning performance, in any learning context. Researchers have been using ML methods in a “black‐box” approach, ie, as a tool where the input data is the learner data and the output is a given class from the chosen construct. This work proposes a methodological shift from the “black‐box” approach to a “grey‐box” approach that bridges the hypothesis/literature‐driven (feature extraction) “white‐box” approach with the computation/data‐driven (feature fusion) “black‐box” approach. This will allow us to utilize data features that are educationally and contextually meaningful. This paper aims to extend current methodological paradigms, and puts into practice the proposed approach in an adaptive self‐assessment case study taking advantage of new, cutting‐edge, interdisciplinary work on building pipelines for educational data, using innovative tools and techniques. What is already known about this topic Capturing and measuring learners' engagement and behavior using physiological data has been explored during the last years and exhibits great potential. Effortless behavioral patterns commonly exhibited by learners, such as “cheating,” “guessing” or “gaming the system” counterfeit the learning outcome. Multimodal data can accurately predict learning engagement, performance and processes. What this paper adds Generalizes a methodology for building machine learning pipelines for multimodal educational data, using a modularized approach, namely the “grey‐box” approach. Showcases that fusion of eye‐tracking, facial expressions and arousal data provide the best prediction of effort and performance in adaptive learning settings. Highlights the importance of fusing data from different channels to obtain the most suited combinations from the different multimodal data streams, to predict and explain effort and performance in terms of pervasiveness, mobility and ubiquity. Implications for practice and/or policy Learning analytics researchers shall be able to use an innovative methodological approach, namely the “grey‐box,” to build machine learning pipelines from multimodal data, taking advantage of artificial intelligence capabilities in any educational context. Learning design professionals shall have the opportunity to fuse specific features of the multimodal data to drive the interpretation of learning outcomes in terms of physiological learner states. The constraints from th...

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Section: Motivation Of the Research And Research Questionmentioning

confidence: 99%

Building pipelines for educational data using AI and multimodal analytics: A “grey‐box” approach

Sharma¹,

Papamitsiou²,

Giannakos³

2019

Brit J Educational Tech

View full text Add to dashboard Cite

show abstract

“…Students are generally satisfied with adaptive quizzing (Becker-Blease & Bostwick, 2016;House, Sweet, & Vickers, 2016) or other adaptive learning technologies (Barla et al, 2010;Griff & Matter, 2013;Liu, McKelroy, Corliss, & Carrigan, 2017) as a learning tool. Students generally report that such technologies are easy to use and perceive that they help to increase their learning of course content.…”

Section: Introductionmentioning

confidence: 99%

Adaptive quizzes to increase motivation, engagement and learning outcomes in a first year accounting unit

Ross

Chase

Robbie

et al. 2018

Int J Educ Technol High Educ

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Adaptive learning presents educators with a possibility of providing learning opportunities tailored to each student's individual needs. As such, adaptive learning may contribute to both improving student learning outcomes and increasing student motivation and engagement. In this paper, we present the findings from a pilot of adaptive quizzes in a fully online unit at an Australian higher education provider. Results indicate that adaptive quizzes contribute to student motivation and engagement, and students perceive that adaptive quizzes support their learning. Interestingly, our results reveal that student scores did not increase significantly as a result of the introduction of adaptive quizzes, indicating that students may not be best placed to assess their own learning outcomes. Despite this, we conclude that adaptive quizzes have value to increase student motivation and engagement.

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“…This finding is consistent with the findings from studies that examined different adaptive systems and compared differences between post-and pre-test scores in relation to improvement in students' learning outcomes. 22,23 However, these studies did not include students who studied using a conventional face-to-face instructional method (i.e., control group). Suebnukarn (2009) compared student learning having used an adaptive learning system in relation to a traditional problem-based learning instructional approach in a single learning session, using within-and betweengroups analyses.…”

Section: Discussionmentioning

confidence: 99%

Effectiveness of an adaptive eLearning intervention on dental students’ learning in comparison to traditional instruction

Alwadei

Tekian

Brown

et al. 2020

Journal of Dental Education

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There is a great deal of excitement in higher education about the value of adaptive learning to personalize learning paths according to students' individual needs. The authors explored the impact of an Adaptive Learning Platform (ALP) on learning, by comparing learning effectiveness between dental students who used the ALP in a blended learning environment formatively and summatively compared with students who did not use the ALP (i.e., face-to-face), as measured by students' performance on the final exam in a single review preparatory course during the academic years 2013-2018. Paired t-tests showed significant improvement in post-test scores across different course instructional modalities (P < 0.01). The learning gain was greater for students who studied using the ALP summatively (t = 26.20) than those who used it formatively or studied using a faceto-face format (t = 13.10 and 14.13, respectively). Controlling for pre-test scores, analysis of covariance tests indicate that: (1) intervention groups (formative and summative ALP) scored significantly higher than the traditional group (B = 9.34 points, P < 0.01, for summative ALP group) and (B = 4.47 points, P < 0.05, for formative ALP group), and (2) summative ALP group scored significantly higher than formative ALP group (B = 4.84 points, P < 0.05). This study provides empirical evidence that an adaptive learning intervention can have a significant impact 1294

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Investigating the effect of an adaptive learning intervention on students’ learning

Cited by 99 publications

References 29 publications

Building pipelines for educational data using AI and multimodal analytics: A “grey‐box” approach

Building pipelines for educational data using AI and multimodal analytics: A “grey‐box” approach

Adaptive quizzes to increase motivation, engagement and learning outcomes in a first year accounting unit

Effectiveness of an adaptive eLearning intervention on dental students’ learning in comparison to traditional instruction

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