A Mixed Methods Analysis of Cognitive Engagement Based on Video Position-Based Notes in Programming Learning

This research full paper describes a study on Video Position-Based Notes (VPB-Notes). Video-based learning environments have become increasingly prevalent in programming education, offering flexibility and personalized pacing. The VPB-Notes which allow students to capture thoughts, questions, and observations at specific video timestamps, have emerged as a promising tool to promote active learning and engagement. However, the lack of standardization in VPB-Notes and the presence of mixed code and natural language poses significant challenges for learning analysis using traditional natural language processing techniques. This study aims to propose a novel analytical framework for understanding the cognitive processes and engagement levels reflected in VPB-Notes taken by students in asynchronous video-based programming courses. The research questions focus on uncovering the key characteristics and patterns of VPB-Notes and investigating the relationships between VPB-Notes features and learners' performance outcomes. Our study combines qualitative analysis of VPB-Notes from four programming courses with quantitative analysis using correlation and cluster analysis. The qualitative analysis reveals that VPB- Notes exhibit contextual relevance, conciseness, dynamic structure, and code integration with commentary. It also proposes an analytical framework for categorizing notes across four dimensions. The quantitative analysis suggests a hierarchy of note features, types, and initiative associated with academic performance. In contrast, cluster analysis highlights the complexity of note portfolios and the influence of personal learning styles. Despite limitations in the automation of note analysis and the need for further validation, this study contributes new insights to the growing body of knowledge on note-taking in online programming education, offering valuable guidance for instructional design and personalized learning support.

Authors

Institutions

Publication Details

Published
2024-10-13
DOI
https://doi.org/10.1109/fie61694.2024.10893458
Citations
3
Primary Topic
Education and Learning Interventions
Type
article
Field-Weighted Citation Impact
2.34
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Mixed Methods Analysis of Cognitive Engagement Based on Video Position-Based Notes in Programming Learning

Xiaonan Wang, Hidenari Kiyomitsu, Yancong Su, Yi Sun et al.
3 citations
Education and Learning Interventions
2.34
article

A Mixed Methods Analysis of Cognitive Engagement Based on Video Position-Based Notes in Programming Learning

Xiaonan Wang, Hidenari Kiyomitsu, Yancong Su, Yi Sun, Takeshi Nishida, Kazuhiro Ohtsuki
article en
3 citations

Abstract

This research full paper describes a study on Video Position-Based Notes (VPB-Notes). Video-based learning environments have become increasingly prevalent in programming education, offering flexibility and personalized pacing. The VPB-Notes which allow students to capture thoughts, questions, and observations at specific video timestamps, have emerged as a promising tool to promote active learning and engagement. However, the lack of standardization in VPB-Notes and the presence of mixed code and natural language poses significant challenges for learning analysis using traditional natural language processing techniques. This study aims to propose a novel analytical framework for understanding the cognitive processes and engagement levels reflected in VPB-Notes taken by students in asynchronous video-based programming courses. The research questions focus on uncovering the key characteristics and patterns of VPB-Notes and investigating the relationships between VPB-Notes features and learners' performance outcomes. Our study combines qualitative analysis of VPB-Notes from four programming courses with quantitative analysis using correlation and cluster analysis. The qualitative analysis reveals that VPB- Notes exhibit contextual relevance, conciseness, dynamic structure, and code integration with commentary. It also proposes an analytical framework for categorizing notes across four dimensions. The quantitative analysis suggests a hierarchy of note features, types, and initiative associated with academic performance. In contrast, cluster analysis highlights the complexity of note portfolios and the influence of personal learning styles. Despite limitations in the automation of note analysis and the need for further validation, this study contributes new insights to the growing body of knowledge on note-taking in online programming education, offering valuable guidance for instructional design and personalized learning support.

Kobe Institute Of Computing (JP), Kobe University (JP), Xiamen University of Technology (CN)
Quality Education
Openalex Percentile: Top 8%
Education and Learning Interventions
2.34
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.