Expectations for supporting student engagement with learning analytics: An academic path perspective

Silvola, Anni; Näykki, Piia; Kaveri, Anceli; Muukkonen, Hanni

doi:10.1016/j.compedu.2021.104192

Cited by 46 publications

(36 citation statements)

References 29 publications

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“…Additionally, online learners are not directly supported by instructors as compared to their counterparts in traditional face-to-face learning. Consequently, there is a need to provide support for SRL and student engagement that is geared towards enhancing self-regulatory skills (Silvola et al, 2021). In view of this, there is need to examine how learners behave and engage in online learning so as to establish the right interventions to be provided to the learners.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In view of this, there is need to examine how learners behave and engage in online learning so as to establish the right interventions to be provided to the learners. Student engagement in online learning, especially behavioral and cognitive aspects, are observable elements that indicate how students participate and get involved in their learning activities (Silvola et al, 2021). SRL, on the other hand, is concerned with learners being proactive in their learning, taking their own initiative to control their learning by setting academic goals and identifying strategies to achieve those goals (Azevedo, 2009;Zimmerman, 1990).…”

Section: Literature Reviewmentioning

confidence: 99%

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Using Educational Data Mining Techniques to Identify Profiles in Self-Regulated Learning: An Empirical Evaluation

Araka

Oboko

Maina

et al. 2022

IRRODL

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With the increased emphasis on the benefits of self-regulated learning (SRL), it is important to make use of the huge amounts of educational data generated from online learning environments to identify the appropriate educational data mining (EDM) techniques that can help explore and understand online learners’ behavioral patterns. Understanding learner behaviors helps us gain more insights into the right types of interventions that can be offered to online learners who currently receive limited support from instructors as compared to their counterparts in traditional face-to-face classrooms. In view of this, our study first identified an optimal EDM algorithm by empirically evaluating the potential of three clustering algorithms (expectation-maximization, agglomerative hierarchical, and k-means) to identify SRL profiles using trace data collected from the Open University of the UK. Results revealed that agglomerative hierarchical was the optimal algorithm, with four clusters. From the four clusters, four SRL profiles were identified: poor self-regulators, intermediate self-regulators, good self-regulators, and exemplary self-regulators. Second, through correlation analysis, our study established that there is a significant relationship between the SRL profiles and students’ final results. Based on our findings, we recommend agglomerative hierarchical as the optimal algorithm to identify SRL profiles in online learning environments. Furthermore, these profiles could provide insights on how to design a learning management system which could promote SRL, based on learner behaviors.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Using Educational Data Mining Techniques to Identify Profiles in Self-Regulated Learning: An Empirical Evaluation

Araka

Oboko

Maina

et al. 2022

IRRODL

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“…Learning analytics is a technology that collects learner data related to learning activities and interprets the data comprehensively using a variety of methods and tools. 7 Through the analysis of these data, we can determine the learning rules of learners and predict the learning results, to provide personalized learning strategies and promote effective learning. Selwyn believed that education-related data cannot well reflect the significance they represent.…”

Section: Introductionmentioning

confidence: 99%

“…These data play a significant role in the proposal of learning analytics technology. Learning analytics is a technology that collects learner data related to learning activities and interprets the data comprehensively using a variety of methods and tools 7 . Through the analysis of these data, we can determine the learning rules of learners and predict the learning results, to provide personalized learning strategies and promote effective learning.…”

Section: Introductionmentioning

confidence: 99%

Real‐time sentiment analysis of students based on mini‐Xception architecture for wisdom classroom

Tian

Tang

Zhu

et al. 2022

Concurrency and Computation

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Sentiment analysis has a wide application prospect in business, medicine, security and other fields, which provides a new perspective for the development of education. Students' sentiment data play an important role in the evaluation of teachers' teaching quality and students' learning effect, and provide a basis for the implementation of effective learning intervention. However, most of the research is to obtain the real-time learning status of students in the classroom through teachers' naked eye observation and students' text feedback, which will lead to some problems such as incomplete feedback content and delayed feedback analysis. Based on the mini-Xception framework, this article implements the real-time identification and analysis of student sentiment in classroom teaching, and the degree of student engagement is analyzed according to the teaching events triggered by teacher to provide reasonable suggestions for subsequent teaching progress. The experimental results show that the mini-Xception model trained by FER2013 data sets has high recognition accuracy for the real-time detection of seven student sentiments, and the average accuracy is 76.71%. Compared with text feedback, it can assist teachers in understanding student learning states in time so that they can take corresponding actions, and realize the real-time performance of wisdom classroom teaching information feedback, the high efficiency of information transmission, and the intelligence of information processing.

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“…Although research on learning analytics is prolific, only a small representation of these studies [13,14] have focused on its impact on future teacher education. In this paper, we present a set of specifications and functionalities that should guide the design and development of the e-learning system to support the practice of future teachers in schools.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Learning Supported by Learning Analytics for Student Teachers’ Personalized Training during in-School Practices

et al. 2021

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This paper presents the results of the second phase of the international project “Improving Educational Innovation, Competitiveness, and Quality of Higher Education through Collaboration between University and Companies (EKT)”. The use of adaptive learning supported by learning analytics is proposed as a pedagogical strategy to work on the collaborative and personalized learning process that takes place during the school placement period of initial teacher education. Learning analytics is expected to facilitate the analysis of the different sources of information and data generated in the learning process. The collected data will be centralized in a learning record store (LRS), which will serve as a repository for xAPI compatible traces from the tools that make up EKT intelligent system. The system is expected to provide a strong support to decision-making so that participant agents can collaborate, advise, and contribute to the future teacher’s personalized training according to his or her progress and the context in which the practice takes place. The need analysis of tutors in the five pilot countries is presented, which has made it possible to define the process variables that make up the learning analysis architecture of the EKT system.

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Expectations for supporting student engagement with learning analytics: An academic path perspective

Cited by 46 publications

References 29 publications

Using Educational Data Mining Techniques to Identify Profiles in Self-Regulated Learning: An Empirical Evaluation

Using Educational Data Mining Techniques to Identify Profiles in Self-Regulated Learning: An Empirical Evaluation

Real‐time sentiment analysis of students based on mini‐Xception architecture for wisdom classroom

Adaptive Learning Supported by Learning Analytics for Student Teachers’ Personalized Training during in-School Practices

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