Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course

Ouyang, Fan; Wu, Mian; Zheng, Luyi; Zhang, Liyin; Jiao, Pengcheng

doi:10.1186/s41239-022-00372-4

Cited by 87 publications

(65 citation statements)

References 50 publications

(72 reference statements)

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“…According to this research, mental load and mental effort are positively correlated and enhance student performance. In addition, Wu (2018) and Yang et al (2023) demonstrated that gamification can facilitate students' comprehension of the material presented on learning platforms and reduce their cognitive load by enhancing playful interactions and increasing engagement. Therefore, based on the findings, this study aims to examine the effect of TTF on cognitive load based on mental load, mental effort, and learning performance.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Examining the Effect of the Task-Technology Fit of Game Mechanisms on Learning Outcomes in Online Gamification Platforms

Wang

Sari

2023

Journal of Educational Computing Research

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The designs of gamification platforms are diverse and constantly evolving. Excessive use of various game mechanisms in learning platforms can distract from the learning process. However, the fit of game mechanisms is still uncertain. Thus, this study investigates the effect of achieving fit when implementing game mechanisms on learning outcomes by applying the well-known task-technology fit theory (TTF). TTF is frequently employed to improve fit between tasks to be completed and the technology applied. The findings indicate that achieving gamification fit can reduce the cognitive load of students and result in enhanced learning performance in terms of learning outcomes. Data collected from 266 participants were analyzed using the technique of the partial least squares to validate the developed research model. The findings of this study can aid educators and educational technology designers in identifying the design mechanisms and characteristics that can be used to ensure design fit on gamification platforms.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Examining the Effect of the Task-Technology Fit of Game Mechanisms on Learning Outcomes in Online Gamification Platforms

Wang

Sari

2023

Journal of Educational Computing Research

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“…Furthermore, different learning analytics approaches reveals different aspects and yields different outcomes (Nistor & Hernández-García, 2018). However, previous studies put emphasis on examining the impact of the learning analytics approach on one dependent variable, such as knowledge building (Yang et al, 2022), group performance (Ouyang et al, 2023), socially shared regulation (Nguyen et al, 2023), behavioural engagement (Yang & Ogata, 2023), and cognitive load (Larmuseau et al, 2020).…”

Section: Learning Analyticsmentioning

confidence: 99%

Impacts of three approaches on collaborative knowledge building, group performance, behavioural engagement, and socially shared regulation in online collaborative learning

Zheng

Fan

Huang

et al. 2023

Computer Assisted Learning

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BackgroundOnline collaborative learning has been widely adopted in the field of education. However, learners often find it difficult to engage in collaboratively building knowledge and jointly regulating online collaborative learning.ObjectivesThe study compared the impacts of the three learning approaches on collaborative knowledge building, group performance, socially shared regulation, behavioural engagement, and cognitive load in an online collaborative learning context. The first is the automatic construction of knowledge graphs (CKG) approach, the second is the automatic analysis of topic distribution (ATD) approach, and the third one is the traditional online collaborative learning (OCL) approach without any analytic feedback.MethodsA total of 144 college students participated in a quasi‐experimental study, where 48 students learned with the CKG approach, 48 students used the ATD approach, and the remaining 48 students adopted the OCL approach.Results and ConclusionsThe findings revealed that the CKG approach could encourage collaborative knowledge building, socially shared regulation, and behavioural engagement in building knowledge better than the ATD and OCL approaches. Both the CKG and ATD approaches could better improve group performance than the OCL approach. Furthermore, the CKG approach did not increase learners' cognitive load, but the ATD approach did.ImplicationsThis study has theoretical and practical implications for utilising learning analytics in online collaborative learning. Furthermore, deep neural network models are powerful for constructing knowledge graphs and analysing topic distribution.

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“…However, not all students can self‐regulate their learning (Kim, Yu, et al, 2023; Larsen et al, 2012). Given the status quo, faculty members wish to identify such students to give them feedback and encourage them to successfully finish their courses (Cohen, 2017; Ouyang et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Unfolding self‐regulated learning profiles of students: A longitudinal study

Esnaashari

Gardner

Arthanari

et al. 2023

Computer Assisted Learning

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Background It is vital to understand students' Self‐Regulatory Learning (SRL) processes, especially in Blended Learning (BL), when students need to be more autonomous in their learning process. In studying SRL, most researchers have followed a variable‐oriented approach. Moreover, little has been known about the unfolding process of students' SRL profiles. Objectives We present the insights derived from a study that measured motivation and the learning strategies used by 198 students of a university entry‐level, business school, BL course to develop an understanding of students' SRL processes. Methods The Strategies for Learning Questionnaire (MSLQ) was used to survey 198 students three times during a semester to investigate SRL profiles and how they unfolded as the course progressed using a person‐oriented approach. Through a clustering approach, we focus on MSLQ's motivation aspects as its importance has been emphasised by different SRL theories, and extant research into motivation in learning analytics (LA) is still lacking. Results and Conclusions Through the longitudinal clustering approach, we identified minimally, average, and highly SRL profiles. We acknowledged that students might change their SRL profiles as the course progressed as a result of feedback they received. What are the 1 or 2 Major Takeaways from the Study? This study contributes to the SRL theory by examining students' SRL profiles adaptation longitudinally (addressing the challenge identified regarding the cyclical nature of SRL). This study contributes to LA by investigating motivational constructs currently lacking in the field and bringing forward theory based empirical evidence to inform theory and practice.

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Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course

Cited by 87 publications

References 50 publications

Examining the Effect of the Task-Technology Fit of Game Mechanisms on Learning Outcomes in Online Gamification Platforms

Examining the Effect of the Task-Technology Fit of Game Mechanisms on Learning Outcomes in Online Gamification Platforms

Impacts of three approaches on collaborative knowledge building, group performance, behavioural engagement, and socially shared regulation in online collaborative learning

Unfolding self‐regulated learning profiles of students: A longitudinal study

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