Learning Analytics-Based Evaluation Mode for Blended Learning and Its Applications

Yi, Baolin; Wang, Yi; Zhang, Dujuan; Liu, Hai; Shu, Jiangbo; Lv, Yuegong

doi:10.1109/iset.2017.42

Cited by 5 publications

(8 citation statements)

References 6 publications

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“…Higher performance students have conceived the meaning of planes and consider the context sustainable in face-to-face learning. Students who haven't been able to develop their spatial conception throughout the virtual CAD I class don't find it sustainable 8 , when returning to normality in a real class environment. The importance of weekly involvement on assignments and tasks along with the MS Teams insights ratio is revealed while students resent on instructors' late gradings has a negative impact on the final grade.…”

Section: Factor Analysis and Discussionmentioning

confidence: 99%

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Analysis of Engineering Student Data in Online Higher Education During the COVID-19 Pandemic

et al. 2021

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The COVID-19 pandemic has challenged many educational institutions around the world in 2020 and 2021 as traditional education has been interrupted to prevent the spread of the virus. This forced the transition from traditional education to fully distance learning envi-ronments for all levels of education. The widespread adoption of distance learning has led instructors to form new digital learning environments and methods. In response to this unexpected situation, data regarding engineering students and their interaction with the learning environment was accumulated and processed, generating a matrix of 129 × 165 variables. The motivation for this research is to identify new variables that impact student performance during the disorientation of the educational process due to the COVID-19 pandemic. Statistical analysis was performed and discussed in this paper including correla-tion analysis, factor analysis, and clustering. Reliability analysis was also performed and ANOVA (analysis of variance) was applied to clusters. The novelty of this work is to use student performance data and statistical analysis of online surveys to reveal patterns that can help reduce dropout rates and transform the educational process, under extenuating and imposed distance learning circumstances. A major finding is that by applying innovative teaching methods, thereby meeting the challenge of an imposed distance learning environ-ment, students' spatial conceptions improve, overcoming the absence of a physical learning space. Deep insights for individual students were discovered, as well as significant relation-ships between students' transition from secondary to higher education and their understand-ing of geometric features. Evidence of the effectiveness of the online learning framework that was integrated showed that it positively influenced students' learning styles.

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Section: Factor Analysis and Discussionmentioning

confidence: 99%

“…Finally, Section 5 presents the paper's conclusions and recommendations for future research steps. Related work Monitoring student progress in online higher education courses has been the subject of new research [8,9], but primarily in the context of exclusively face-to-face or online teaching environments.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Engineering Student Data in Online Higher Education During the COVID-19 Pandemic

et al. 2021

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“…Log data of student activities in the discussion forum (Ifenthaler & Yau, 2020), (Yi et al, 2017), (Chen et al, 2018), (Perez-Alvarez et al, 2020), (Anderson et al, 2016) Online Course logs can be taken of the student interaction in the discussion forum Log files and trace data of digital learning interaction. (Ifenthaler & Yau, 2020), (Perez-Alvarez et al, 2020) Online Course logs are taken during learners' interaction with course materials.…”

Section: Data Source References Descriptionmentioning

confidence: 99%

A Comprehensive Survey on Usage of Learning Analytics for Enhancing Learner's Performance in Learning Portals

Shabnam Ara,

Tanuja,

Manjula

et al. 2023

Journal of Educational Technology Systems

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Learning analytics (LA) is considered a promising field of study as it's helping to improve learning and the context in which it occurs. A learner's performance can be defined as how well students are learning in terms of knowledge and skills development and can be analyzed based on students’ outcomes and engagement in the course. We have consolidated the work carried out from 2011 to 2022 to improve learners’ performance using LA, describe criteria that define learners’ performance, discuss parameters that impact learners’ performance, and how predictive models can be created to forecast learners’ performance using these parameters. Results showed that the data collected from log files of the Learning Management System (LMS) had been used to get insights into the learner's performance in online platforms and LA could bring incredible benefits in the field of the education sector, such as improvement of learners’ involvement with learning activities as well as learning outcomes, identification of students at risk, providing real-time feedback, and personalization of learning. Hence, we can say usage of LA significantly helps learners’ performance improvement in learning portals. But we can get better results if we augment data from log files of LMS with the learner's personal data from his birth to the current moment, which is a bit challenging with respect to data collection i.e., huge and from multiple sources.

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“…Evaluating and monitoring students' learning is regarded as an essential aspect of higher education [6]. Evaluation is the judgment of summative and procedural learning results and is helpful in teaching decision-making [19]. However, how to achieve practical evaluation in online learning remains a difficult problem for teachers.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Dimensional Assessment Model and Its Application in E-learning Courses of Computer Science

Luo

Feng

Wang

2020

Proceedings of the 21st Annual Conference on Information Technology Education

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Computer science is a practical discipline. It is always a great challenge to evaluate students' computer practice using computer-aided means for large scale students. We always need to address problems such as suspected plagiarism and deviation of the overall difficulty factor. In this paper, a multi-dimensional assessment model is designed for CS courses based on the detailed practice processing data in an E-learning system. The model comprehensively evaluates the students' learning process and results in three aspects of correctness, originality, and quality detection. Besides, the teacher can easily participate in the assessment according to their needs. The correctness is an essential requirement, and the originality is based on the clustering results of students' behaviors after clone detection to curb homework plagiarism. SonarQube is used to detect code quality and put forward higher requirements for codes. Manual participation intelligence has improved the flexibility and applicability of the model to a certain extent. We applied this model on the EduCoder online education platform and carried out a comprehensive analysis of 485 students in the Parallel Programming Principles and Practice Class of Huazhong University of Science and Technology. Experiment results confirm the distinction, rationality, and fairness of the model in assessing student performance. It not only gives students a credible, comprehensive score in largescale online practical programming courses but also gives teachers and students corresponding suggestions based on the evaluation results. Furthermore, the model can be extended to other online education platforms.

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Learning Analytics-Based Evaluation Mode for Blended Learning and Its Applications

Cited by 5 publications

References 6 publications

Analysis of Engineering Student Data in Online Higher Education During the COVID-19 Pandemic

Analysis of Engineering Student Data in Online Higher Education During the COVID-19 Pandemic

A Comprehensive Survey on Usage of Learning Analytics for Enhancing Learner's Performance in Learning Portals

A Multi-Dimensional Assessment Model and Its Application in E-learning Courses of Computer Science

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