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Kukreja, Vinay
- What Factors Impact Online Education? A Factor Analysis Approach
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1 Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, IN
1 Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, IN
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Journal of Engineering Education Transformations, Vol 34, No SP ICTIEE (2021), Pagination: 365-374Abstract
Internet popularity is growing day by day, which directly or indirectly increases the potential for online study. Apart from this, the current era of COVID 19 has revolutionized online education to a greater extent. However, the usage of computers, software's and technological advancement has raised many questions about the effectiveness of online platforms and online study. This survey-based quantitative study aimed to investigate the factors that affect the success of the online study. A total of 690 participants have participated in the survey attending online classes from multiple institutions in India. After careful investigation, 673 participants survey that was complete in all aspects has been considered for analysis. These students were either graduate/post-graduate or doing graduation/ post-graduate. The data were collected using a structured questionnaire (see Annexure A). Exploratory Factor analysis (EFA) has been conducted to figure out the factors that have positively impacted the students' satisfaction with online study. Confirmatory Factor Analysis (CFA) has been performed to confirm the factors. Results of the structural equation modeling show that instructor quality, course design, ICT orientation, conscientiousness, open-mindedness, and agreeableness have a positive impact and extraversion has a negative impact on the satisfaction of the students. The neurotic type of personality has no impact on the satisfaction of the students.Keywords
Course Design, Instructor Quality, Student Traits, ICT Orientation, Student Satisfaction, Online Classes, COVID 19.Full Text
- Multi-Expert and Multi-Criteria Evaluation of Online Education Factors: A Fuzzy AHP Approach
Abstract Views :259 |
PDF Views:108
Authors
Affiliations
1 Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, IN
2 Chitkara Business School, Chitkara University, Punjab, IN
1 Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, IN
2 Chitkara Business School, Chitkara University, Punjab, IN
Source
Journal of Engineering Education Transformations, Vol 35, No 2 (2021), Pagination: 140-148Abstract
COVID-19 has highly impacted industry, agriculture, services sector as well as education sector all over the world. The countries have seen a complete lockdown, and it has badly affected students' lives in the education sector. Almost more than 32 crores of learners are unable to move to schools or colleges in India. The solution to overcome the offline education crisis is to move to online platforms. But, the effectiveness of online platforms for teaching is a big challenge. The most important thing in teaching is achieving the satisfaction level of students. The literature shows many factors impact satisfaction level, and these factors are ICT orientation, Big-Five Personality Dimensions, Instructor Quality, and Course Design. These factors are having subfactors four, five, seven, and six, respectively. The current study targets to prioritize the factors by using the fuzzy AHP approach. The factors are pritorized based on their normalized weight. To gain depth insights, the sub-factors are also prioritized, and they are ranked relatively as well as globally. Relatively means to figure out the important and least sub-factor from the corresponding factor, globally means to rank each sub-factor among all identified factors. The results show that BF is the most important and CD is the least important factor for achieving students satisfaction level. Looking at relative weights, NE and LQ are the most important factors among BF and CD, respectively. After considering global weights, PI and AD are the most and least important sub-factors, respectively.Keywords
Online Classes, Fuzzy AHP, Instructor Quality, ICT Orientation, COVID 19.Full Text
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- Fuzzy AHP-TOPSIS Approaches to Prioritize Teaching Solutions for Intellect Errors
Abstract Views :139 |
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1 Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, IN
1 Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, IN
Source
Journal of Engineering Education Transformations, Vol 35, No 4 (2022), Pagination: 50-58Abstract
The teaching fraternity and intellects play an important role in students’ careers as they make students industry-ready. During their teaching, they make different types of errors. One of the neglected aspects during teaching is intellect errors and these directly or indirectly impact students learning capabilities. The scattered literature shows that there are twelve types of intellect errors like ‘error of coincidence’, ‘senses error’, ‘analogy error’, ‘subjectivity error’, etc. To minimize these errors, six solutions have been identified like ‘selection of right instruments’, ‘development of critical thinking in the students’, ‘aware about knowledge engineering development’ etc. This study aims to identify and prioritize the solutions to overcome the errors of the intellect that has been the ignored aspect of the teaching till now. A hybrid approach of fuzzy AHP (Analytical Hierarchy Process) and Fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) has been proposed to rank the solutions that minimize the intellect errors. Fuzzy AHP is used to compute the weights for intellect errors by doing the pairwise comparison and fuzzy TOPSIS is used to rank the identified solutions with the help of generated weights of fuzzy AHP. The results show that “error of proximity” and “senses error” are the highest and least rated intellect errors respectively. The topmost rated solution to handle errors of the intellect is “development of critical thinking in the students”.Keywords
Intellect Errors, Fuzzy AHP, Fuzzy TOPSIS, Industry-Ready.
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- Identification of Effective Scaffolding to Novices Using CBLE
Abstract Views :148 |
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Authors
Affiliations
1 Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, IN
1 Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, IN
Source
Journal of Engineering Education Transformations, Vol 35, No 4 (2022), Pagination: 95-103Abstract
The aim of this study is to discover which kind of scaffolding can effectively promote learning. The past studies have shown mixed results in this regard. The process in which a domain expert gives and withdraws support in order to make a novice learner complete the task is known as scaffolding. A total of four distinct scaffold combinations and four groups were made. This experimental study was repeated twice to cross verify the outcomes using computer based learning environment (CBLE). The CBLE was designed with intelligent web program in PHP and jQuery to evaluate the solutions submitted by the learners instantly. The CBLE acted as an intelligent feedback system. In the first study, it was found that there was a significant effect of different scaffolding treatments on the learning outcomes, F (3,76) = 5.762, p=.001. The result analysis involves multiple comparisons based on Tukey HSD test and indicated that the mean score for the indirect support and adaptive fading (M=4.45, SD=1.191) was considerably different than the others. Likewise, second study also found that there was a significant effect of different scaffold treatments on the learning outcome, F (3,76) = 4.258, p=.008. The Tukey HSD test applied during the second study indicated that the mean score for the indirect support and adaptive fading (M=4.55, SD=1.19) was again significantly different than the others. The present study additionally measured the flow state of all the four groups using Kruskal-Wallis H test and found that indirect support and adaptive fading group was significantly different than direct support and adapting fading group as well as direct support and gradual fading group in both the studies.Keywords
Computer Based Learning Environment (CBLE), Effective Scaffolding, Intelligent Feedback System.References
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