Design science research applied to difficulties of teaching and learning initial programming

Learning and teaching to program is an arduous task. It requires a lot of commitment, dedication, and passion from everyone involved. Programming courses have high dropout and failure rates. Throughout time, several educational research works have been carried out to study the different learning pro...

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Veröffentlicht in:Universal access in the information society Jg. 23; H. 3; S. 1151 - 1161
Hauptverfasser: Figueiredo, José, García-Peñalvo, Francisco José
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2024
Springer Nature B.V
Schlagworte:
Academic failure
Computer Communication Networks
Computer Science
Computers and Society
Educational research
Failure rates
Information Storage and Retrieval
Information Systems Applications (incl.Internet)
IT in Business
Learning
Long Paper
Machine learning
Neural networks
Prediction models
Predictions
Programming
Students
Teachers
Teaching
User Interfaces and Human Computer Interaction
CS1
Programming
CS0
Design science research methodology
Learning programming
Teaching programming
ISSN:1615-5289, 1615-5297, 1615-5297
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Zusammenfassung:Learning and teaching to program is an arduous task. It requires a lot of commitment, dedication, and passion from everyone involved. Programming courses have high dropout and failure rates. Throughout time, several educational research works have been carried out to study the different learning processes and characteristics of students. With this work, we present and describe our vision and model of teaching and learning of initial programming to minimize the problems. We present a technological tool, called HTProgramming (Help To Programming), which complements the teaching and learning process. This allows students to practice a wide variety of activities with immediate feedback, directly related to content and themes for learning programming. It allows the teacher to follow the whole process and students’ results. Using a machine-learning (neural network) predictive model of student failure, it will allow the teacher to anticipate possible student failure and act quickly. In this paper, we apply the Design Scientific Research Methodology to tackle teaching and learning difficulties to initial programming. We also include the results and evaluation of the application. Students consider the application an important tool for their learning process. The student failure prediction model presents very realistic values.
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ISSN:1615-5289
1615-5297
1615-5297
DOI:10.1007/s10209-022-00941-4