Abstract
Inductive Teaching Method (ITM) promotes effective learning in technological education (Felder & Silverman, 1988). Students prefer ITM more as it makes the subject easily understandable (Goltermann, 2011). The ITM motivates the students to actively participate in class activities and therefore could be considered a better approach to teach computer programming. There has been little research on implementing ITM in computer science courses despite its potential to improve effective learning. In this research, an existing computer programming lab course is taught using a traditional Deductive Teaching Method (DTM). The course is redesigned and taught by adopting the ITM instead. Furthermore, a comprehensive plan has been devised to deliver the course content in computer labs. The course was evaluated in an experiment consisting of 81 undergraduate students. The students in the Experimental Group (EG) (N = 45) were taught using the redesigned ITM course, whereas the students in the Control Group (CG) (N = 36) were taught using the DTM course. The performance of both groups was compared in terms of the marks obtained by them. A pre-test conducted to compare pre-course mathematical and analytical abilities showed that CG was better in analytical reasoning with no significant differences in mathematical abilities. Three post-tests were used to evaluate the groups theoretical and practical competence in programming and showed EG improved performance with large, medium, and small effect sizes as compared to CG. The results of this research could help computer programming educators to implement inductive strategies that could improve the learning of the computer programming.
Highlights
With the steady growth in automation and digitalization, there is a shortage of Computer Science (CS) graduates [1]
The effectiveness of the designed Inductive Teaching Method (ITM) course was validated by implementing it in a regular semester and comparing it with a Deductive Teaching Method (DTM) course using an Experimental Group (EG) and a Control Group (CG) respectively
The analytical portion of the pre-test revealed that the CG was significantly better in analytical reasoning as compared to the EG
Summary
With the steady growth in automation and digitalization, there is a shortage of Computer Science (CS) graduates [1]. The Forbes Technology Council [2] identified 13 technology skills needed in the job market in 2018, and 11 of these skills directly or indirectly involve computer programming. This shows a greater employability potential of CS graduates. Ironically many CS graduates are unemployed [3]. One of the reasons for the unemployability.
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