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...

Full description

Saved in:

Bibliographic Details
Main Author:	Shahri, Norsyarizan
Format:	Thesis
Language:	English
Published:	2020
Subjects:	TP Chemical technology
Online Access:	http://eprints.utm.my/id/eprint/102283/1/NorsyarizanShahriPSChE2020.pdf.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utm-ep.102283
record_format	uketd_dc
spelling	my-utm-ep.1022832023-08-17T00:24:12Z Effects of deep learning module on students achievement in programmable logic controller course 2020 Shahri, Norsyarizan TP Chemical technology 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. 2020 Thesis http://eprints.utm.my/id/eprint/102283/ http://eprints.utm.my/id/eprint/102283/1/NorsyarizanShahriPSChE2020.pdf.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:149080 phd doctoral Universiti Teknologi Malaysia Faculty of Engineering - School of Chemical & Energy Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TP Chemical technology
spellingShingle	TP Chemical technology Shahri, Norsyarizan Effects of deep learning module on students achievement in programmable logic controller course
description	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.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Shahri, Norsyarizan
author_facet	Shahri, Norsyarizan
author_sort	Shahri, Norsyarizan
title	Effects of deep learning module on students achievement in programmable logic controller course
title_short	Effects of deep learning module on students achievement in programmable logic controller course
title_full	Effects of deep learning module on students achievement in programmable logic controller course
title_fullStr	Effects of deep learning module on students achievement in programmable logic controller course
title_full_unstemmed	Effects of deep learning module on students achievement in programmable logic controller course
title_sort	effects of deep learning module on students achievement in programmable logic controller course
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Engineering - School of Chemical & Energy Engineering
publishDate	2020
url	http://eprints.utm.my/id/eprint/102283/1/NorsyarizanShahriPSChE2020.pdf.pdf
_version_	1776100886943105024

Effects of deep learning module on students achievement in programmable logic controller course

Similar Items