scholarly journals Learning Analytics Using Social Network Analysis and Bayesian Network Analysis in Sustainable Computer-Based Formative Assessment System

2020 ◽  
Vol 12 (19) ◽  
pp. 7950 ◽  
Author(s):  
Younyoung Choi ◽  
Young Il Cho
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Bayesian Network ◽  
Learning Analytics ◽  
Evaluation System ◽  
Analytic Method ◽  
Learning Skills ◽  
21St Century Learning Skills ◽  
Computer Based

The sustainable computer-based evaluation system (SCE) is a scenario-based formative evaluation system, in which students are assigned a task during a course. The tasks include the diversity conditions in real-world scenarios. The goals of this system are learning to think as a professional in a certain discipline. While the substantive, psychological, instructional, and task developmental aspects of the assessment have been investigated, few analytic methods have been proposed that allow us to provide feedback to learners in a formative way. The purpose of this paper is to introduce a framework of a learning analytic method including (1) an assessment design through evidence-centered design (ECD), (2) a data mining method using social network analysis, and (3) an analytic method using a Bayesian network. This analytic framework can analyze the learners’ performances based on a computational psychometric framework. The tasks were designed to measure 21st century learning skills. The 250 samples of data collected from the system were analyzed. The results from the social network analysis provide the learning path during a course. In addition, the 21st century learning skills of each learner were inferred from the Bayesian network over multiple time points. Therefore, the learning analytics proposed in this study can offer the student learning progression as well as effective feedback for learning.

Bridging the Gap between LMS and Social Network Learning Analytics in Online Learning

2016 ◽  
Vol 9 (4) ◽  
pp. 1-15 ◽  
Author(s):  
Ángel Hernández-García ◽  
Miguel Ángel Conde-González
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Collaborative Learning ◽  
Learning Analytics ◽  
Real Data ◽  
General Purpose ◽  
Computer Supported Collaborative Learning ◽  
Message Boards ◽  
Social Graphs

Despite the great potential of social network analysis (SNA) methods and visualizations for learning analytics in computer-supported collaborative learning (CSCL), these approaches have not been fully explored due to two important barriers: the scarcity and limited functionality of built-in tools in Learning Management Systems (LMS), and the difficulty to import educational data from formal virtual learning environments into social network analysis programs. This study aims to cover that gap by introducing GraphFES, an application and web service for extraction of interaction data from Moodle message boards and generation of the corresponding social graphs for later analysis using Gephi, a general purpose SNA software. In addition, this paper briefly illustrates the potential of the combination of the three systems (Moodle, GraphFES and Gephi) for social learning analytics using real data from a computer-supported collaborative learning course with strong focus on teamwork and intensive use of forums.

scholarly journals Student’s Performance Prediction Using Hybrid Optimization Algorithm-based Map Reduce Framework

2021 ◽  
Author(s):  
Dipak Bhosale ◽  
Mahendra Sawane
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Student Performance ◽  
Language Processing ◽  
Learning Analytics ◽  
Concept Maps ◽  
Machine Learning Techniques ◽  
Planning And Scheduling ◽  
Student’S Performance

Learning analytics (LA) is a growing research area, which aims at selecting, analyzing and reporting student data (in their interaction with the online learning environment), finding patterns in student behaviour, displaying relevant information in suggestive formats; the end goal is the prediction of student performance, the optimization of the educational platform and the implementation of personalized interventions. According to the Society of Learning Analytics Research1, LA can be defined as "the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs". The topic is highly interdisciplinary, including machine learning techniques, educational data mining, statistical analysis, social network analysis, natural language processing, but also knowledge from learning sciences, pedagogy and sociology; up-to-date overviews of the area are provided in. Various educational tasks can be supported by learning analytics, as identified in analysis and visualization of data; providing feedback for supporting instructors; providing recommendations for students; predicting student's performance; student modelling; detecting undesirable student behaviours; grouping students; social network analysis; developing concept maps; constructing courseware; planning and scheduling. Similarly, seven main objectives of learning analytics are summarized in: monitoring and analysis; prediction and intervention; tutoring and mentoring; assessment and feedback; adaptation; personalization and recommendation; reflection.

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