Adapting Video-Based Programming Instruction: An Empirical Study Using a Decision Tree Learning Model
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| Title: | Adapting Video-Based Programming Instruction: An Empirical Study Using a Decision Tree Learning Model |
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| Language: | English |
| Authors: | Sanal Kumar T. S., R. Thandeeswaran |
| Source: | Education and Information Technologies. 2024 29(11):14205-14243. |
| Availability: | Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/ |
| Peer Reviewed: | Y |
| Page Count: | 39 |
| Publication Date: | 2024 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Blended Learning, Electronic Learning, Higher Education, Programming, Programming Languages, Video Technology, Technology Uses in Education, Computer Science Education, Educational Technology, Cognitive Style, Preferences, College Freshmen, Instructional Effectiveness, Feedback (Response) |
| DOI: | 10.1007/s10639-023-12390-4 |
| ISSN: | 1360-2357 1573-7608 |
| Abstract: | The COVID-19 pandemic has forced a significant increase in the utilization of video-based e-learning platforms for programming education. These platforms never considered the essential attributes of student characteristics and learning preferences while designing such a problematic subject having high dropout and failure rates. The traditional e-learning environments deliver instructional videos to the learners by assuming all learners have a single learning preference. Moreover, existing learning style models need to address the recent requirements of e-learning paradigms. To address this issue, this paper presents a novel learning style model tailored for instructional video-based programming e-learning environments that map individual learning preferences with various video design patterns. An adaptive e-learning environment was employed to assess the effectiveness of the proposed model that leveraged a decision tree classifier to divide learners into four preferences. In a paired experimental design, 195 first-year undergraduate students were randomly assigned to one of three groups where learner scores and feedback were taken as evaluation metrics. The control group partook without instructional videos for the entire semester of six months. During the same period, experimental group-1 learned with a traditional video-based e-learning environment, and experimental group-2, with the proposed learning style model, enabled an adaptive e-learning environment. Based on the proposed decision tree learning model, it is understood that the intervention group showed significant improvements in knowledge acquisition, grade, and positive feedback compared to the other groups. Hence, the proposed model is highly recommended for traditional programming e-learning environments to deliver instructional videos based on learners' learning preferences. |
| Abstractor: | As Provided |
| Entry Date: | 2024 |
| Accession Number: | EJ1437303 |
| Database: | ERIC |
| Abstract: | The COVID-19 pandemic has forced a significant increase in the utilization of video-based e-learning platforms for programming education. These platforms never considered the essential attributes of student characteristics and learning preferences while designing such a problematic subject having high dropout and failure rates. The traditional e-learning environments deliver instructional videos to the learners by assuming all learners have a single learning preference. Moreover, existing learning style models need to address the recent requirements of e-learning paradigms. To address this issue, this paper presents a novel learning style model tailored for instructional video-based programming e-learning environments that map individual learning preferences with various video design patterns. An adaptive e-learning environment was employed to assess the effectiveness of the proposed model that leveraged a decision tree classifier to divide learners into four preferences. In a paired experimental design, 195 first-year undergraduate students were randomly assigned to one of three groups where learner scores and feedback were taken as evaluation metrics. The control group partook without instructional videos for the entire semester of six months. During the same period, experimental group-1 learned with a traditional video-based e-learning environment, and experimental group-2, with the proposed learning style model, enabled an adaptive e-learning environment. Based on the proposed decision tree learning model, it is understood that the intervention group showed significant improvements in knowledge acquisition, grade, and positive feedback compared to the other groups. Hence, the proposed model is highly recommended for traditional programming e-learning environments to deliver instructional videos based on learners' learning preferences. |
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| ISSN: | 1360-2357 1573-7608 |
| DOI: | 10.1007/s10639-023-12390-4 |
