Effects of deep learning module on students achievement in programmable logic controller course
Learning programming for engineering technology courses is challenging as novice programmers have limited understanding of the basic concepts, and therefore have difficulty applying them. Students found that Programmable Logic Controller (PLC), a core technical course taught in Kolej Kemahiran Tingg...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/102283/1/NorsyarizanShahriPSChE2020.pdf.pdf |
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| Summary: | Learning programming for engineering technology courses is challenging as novice programmers have limited understanding of the basic concepts, and therefore have difficulty applying them. Students found that Programmable Logic Controller (PLC), a core technical course taught in Kolej Kemahiran Tinggi MARA (KKTM), Malaysia is difficult. Preliminary study conducted at KKTM found that most students used a surface learning approach for PLC, a pre-requisite course to other programming courses; therefore, they found difficult to do other programming courses as they have weak understanding on the fundamental knowledge in programming. Thus, the aim of this study is to develop deep learning module that encourage students’ engagement in learning programming courses. A total of 68 second-year students from Industrial Mechatronics Engineering Technology Program participated in the study. The students were divided into two groups: 33 students in a control group and 35 students in a treatment group to examine Student Approach to Learning (SAL) in the PLC course. The adapted R-SPQ-2F questionnaires and interviews were used to measure the differences of deep learning scores before and after the intervention. The intervention strategies comprised two phases. The intervention in the treatment group was audio-video recorded. Phase one involved cooperative learning strategies, think-pair share in teaching concept inventory questions and jigsaw in programming exercises. Phase two intervention used open-ended questions with adaptations of engineering thinking and reasoning concepts. Think aloud method was used by the students to record their project assignments using audio video screen capture software. In addition, students were asked to update their learning progress weekly using suggested learning verbs of Bloom’s taxonomy into SOLO’s map application. Results from the video observation in phase one indicated students engagement and interest in learning. In addition, students reported that the strategies in phase two intervention helped to enhance their thinking and reasoning which lead to a deeper learning. Self-assessment R-SPQ-2F results of the t-test for the treatment group had a positive effect (less than 0.05) towards deep learning approach compared to the control group. In addition, SOLO’s map final results indicated that 85% of the students were able to achieve a ‘relational level’ of deep learning stage. Interview results showed that students started to adapt to the deep learning approach in phase 2. The findings support that the developed deep learning module used during the intervention helps to enhance students’ learning in programming courses. |
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