A Learning Analytics System for Cognition Analysis in Online Learning Community

Wu, Yafang; Wu, Wenjun

doi:10.1007/978-3-030-01298-4_21

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…While it is not the aim to provide an exhaustive review of the extensive literature on the topic, several key aspects can be highlighted. These include academic performance, student retention, motivation (Lonn et al, 2015), engagement (Coffrin et al, 2014), learning gains, satisfaction (Elia et al, 2019), metacognitive skills (Wu & Wu, 2018), and self-regulated learning ability, which is determined by analyzing individual records of academic performance, interactions with course content, and personal information (Kizilcec et al, 2017). Researchers have proposed various models for data analysis and the development of personalized feedback systems (Arnold & Pistilli, 2012), as well as predictive models to identify at-risk students (Monllaó Olivé et al, 2020).…”

Section: Learning Analyticsmentioning

confidence: 99%

Discovering Insights in Learning Analytics Through a Mixed-Methods Framework: Application to Computer Programming Education

Johanna Chaparro Amaya,

Restrepo-Calle,

J Ramírez-Echeverry

2023

JITE:Research

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Aim/Purpose: This article proposes a framework based on a sequential explanatory mixed-methods design in the learning analytics domain to enhance the models used to support the success of the learning process and the learner. The framework consists of three main phases: (1) quantitative data analysis; (2) qualitative data analysis; and (3) integration and discussion of results. Furthermore, we illustrated the application of this framework by examining the relationships between learning process metrics and academic performance in the subject of Computer Programming coupled with content analysis of the responses to a students’ perception questionnaire of their learning experiences in this subject. Background: There is a prevalence of quantitative research designs in learning analytics, which limits the understanding of students’ learning processes. This is due to the abundance and ease of collection of quantitative data in virtual environments and learning management systems compared to qualitative data. Methodology: This study uses a mixed-methods, non-experimental, research design. The quantitative phase of the framework aims to analyze the data to identify behaviors, trends, and relationships between measures using correlation or regression analysis. On the other hand, the qualitative phase of the framework focuses on conducting a content analysis of the qualitative data. This framework was applied to historical quantitative and qualitative data from students’ use of an automated feedback and evaluation platform for programming exercises in a programming course at the National University of Colombia during 2019 and 2020. The research question of this study is: How can mixed-methods research applied to learning analytics generate a better understanding of the relationships between the variables generated throughout the learning process and the academic performance of students in the subject of Computer Programming? Contribution: The main contribution of this work is the proposal of a mixed-methods learning analytics framework applicable to computer programming courses, which allows for complementing, corroborating, or refuting quantitatively evidenced results with qualitative data and generating hypotheses about possible causes or explanations for student behavior. In addition, the results provide a better understanding of the learning processes in the Computer Programming course at the National University of Colombia. Findings: A framework based on sequential explanatory mixed-methods design in the field of learning analytics has been proposed to improve the models used to support the success of the learning process and the learner. The answer to the research question posed corresponds to that the mixed methods effectively complement quantitative and qualitative data. From the analysis of the data of the application of the framework, it appears that the qualitative data, representing the perceptions of the students, generally supported and extended the quantitative data. The consistency between the two phases allowed us to generate hypotheses about the possible causes of student behavior and provide a better understanding of the learning processes in the course. Recommendations for Practitioners: We suggest implementing the proposed mixed-methods learning analytics framework in various educational contexts and populations. By doing so, practitioners can gather more diverse data and insights, which can lead to a better understanding of learning processes in different settings and with different groups of learners. Recommendation for Researchers: Researchers can use the proposed approach in their learning analytics projects, usually based exclusively on quantitative data analysis, to complement their results, find explanations for their students’ behaviors, and understand learning processes in depth thanks to the information provided by the complementary analysis of qualitative data. Impact on Society: The prevalence of exclusively quantitative research designs in learning analytics can limit our understanding of students’ learning processes. Instead, the mixed-methods approach we propose suggests a more comprehensive approach to learning analytics that includes qualitative data, which can provide deeper insight into students’ learning experiences and processes. Ultimately, this can lead to more effective interventions and improvements in teaching and learning practices. Future Research: Potential lines of research to continue the work on mixed-method learning analytics methodology include the following: first, implementing the framework on a different population sample, such as students from other universities or other knowledge areas; second, using techniques to correct unbalanced data sets in learning analytics studies; third, analyzing student interactions with the automated grading platform and their academic activities in relation with their activity grades; last, using the findings to design interventions that positively impact academic performance and evaluating the impact statistically through experimental study designs. In the context of introductory programming education, AI/large language models have the potential to revolutionize teaching by enhancing the learning experience, providing personalized support, and enabling more efficient assessment and feedback mechanisms. Future research in this area is to implement the proposed framework on data from an introductory programming course using these models.

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Section: Learning Analyticsmentioning

confidence: 99%

Discovering Insights in Learning Analytics Through a Mixed-Methods Framework: Application to Computer Programming Education

Johanna Chaparro Amaya,

Restrepo-Calle,

J Ramírez-Echeverry

2023

JITE:Research

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“…So, in order to give full play to the real effects of hybrid learning, Hybrid Learning Community Network (HLCN) could be built to integrate online and offline learning and form hybrid learning network communities [14][15][16][17]. HLCN can link individuals to collectives systematically, support both virtual and actual scenarios, and share and trade knowledge, experience, and learning resources [18][19][20][21]. Forming learning communities with common group learning goals based on the learning community theory and exploring innovative hybrid teaching modes are of great significance for the knowledge sharing and innovation of HLCN.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Effectiveness of Hybrid Learning Activities Based on a Learning Community Network

Xie¹

2022

Int. J. Emerg. Technol. Learn.

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Hybrid Learning Community Network (HLCN) can link individuals to collectives systematically, support both virtual and actual scenarios, and share and trade knowledge, experience, and learning resources. Existing studies on HLCN are mostly theoretical research, there’re very few empirical evidences, and the integrity of the research systems is yet to be improved further. In view of these shortcomings, this paper proposed a theoretical model for HLCN and built an Evaluation Index System (EIS) for assessing the effectiveness of the hybrid learning activities and analyzing the feature parameters and centrality of the HLCN. Then in the paper, a Hybrid Learning Interactive Activity Content Network Relation (HL-IAC-NR) matrix was established based on learning community, and the indexes such as students’ learning attention on knowledge points and their learning sentiments were attained. At last, this paper used the Charnes-Cooper-Rhodes (CCR) model in Data Envelope Analysis (DEA) to evaluate the relative effectiveness, and the real-time effects of different types of hybrid learning interactive activities were analyzed. The effectiveness of the proposed model and evaluation method was verified via experiments.

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Automatic content analysis of asynchronous discussion forum transcripts: A systematic literature review

2022

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A Learning Analytics System for Cognition Analysis in Online Learning Community

Cited by 4 publications

References 15 publications

Discovering Insights in Learning Analytics Through a Mixed-Methods Framework: Application to Computer Programming Education

Discovering Insights in Learning Analytics Through a Mixed-Methods Framework: Application to Computer Programming Education

Evaluation of the Effectiveness of Hybrid Learning Activities Based on a Learning Community Network

Automatic content analysis of asynchronous discussion forum transcripts: A systematic literature review

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