Automatically Detecting Previous Programming Knowledge from Novice Programmer Code Compilation History
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| Title: | Automatically Detecting Previous Programming Knowledge from Novice Programmer Code Compilation History |
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| Language: | English |
| Authors: | Lokkila, Erno, Christopoulos, Athanasios, Laakso, Mikko-Jussi |
| Source: | Informatics in Education. 2023 22(2):277-294. |
| Availability: | Vilnius University Institute of Mathematics and Informatics, Lithuanian Academy of Sciences. Akademjos str. 4, Vilnius LT 08663 Lithuania. Tel: +37-5-21-09300; Fax: +37-5-27-29209; e-mail: info@mii.vu.lt; Web site: https://infedu.vu.lt/journal/INFEDU |
| Peer Reviewed: | Y |
| Page Count: | 18 |
| Publication Date: | 2023 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Prior Learning, Programming, Computer Science Education, Markov Processes, Likert Scales, Introductory Courses, Novices, Algorithms, Student Behavior, Teaching Methods, Learning Processes, Programming Languages, Undergraduate Students, Artificial Intelligence, Foreign Countries |
| Geographic Terms: | Finland |
| ISSN: | 1648-5831 2335-8971 |
| Abstract: | Prior programming knowledge of students has a major impact on introductory programming courses. Those with prior experience often seem to breeze through the course. Those without prior experience see others breeze through the course and disengage from the material or drop out. The purpose of this study is to demonstrate that novice student programming behavior can be modeled as a Markov process. The resulting transition matrix can then be used in machine learning algorithms to create clusters of similarly behaving students. We describe in detail the state machine used in the Markov process and how to compute the transition matrix. We compute the transition matrix for 665 students and cluster them using the k-means clustering algorithm. We choose the number of cluster to be three based on analysis of the dataset. We show that the created clusters have statistically different means for student prior knowledge in programming, when measured on a Likert scale of 1-5. |
| Abstractor: | As Provided |
| Entry Date: | 2023 |
| Accession Number: | EJ1392996 |
| Database: | ERIC |
| Abstract: | Prior programming knowledge of students has a major impact on introductory programming courses. Those with prior experience often seem to breeze through the course. Those without prior experience see others breeze through the course and disengage from the material or drop out. The purpose of this study is to demonstrate that novice student programming behavior can be modeled as a Markov process. The resulting transition matrix can then be used in machine learning algorithms to create clusters of similarly behaving students. We describe in detail the state machine used in the Markov process and how to compute the transition matrix. We compute the transition matrix for 665 students and cluster them using the k-means clustering algorithm. We choose the number of cluster to be three based on analysis of the dataset. We show that the created clusters have statistically different means for student prior knowledge in programming, when measured on a Likert scale of 1-5. |
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| ISSN: | 1648-5831 2335-8971 |
