Please use this identifier to cite or link to this item: https://hdl.handle.net/10321/4107
Title: Intelligent decision support system for selection of Learning Apps to promote critical thinking in first year programming students
Authors: Singh, Kesarie 
Keywords: Block-based programming;Computational thinking;Critical thinking;Decision theory
Issue Date: 9-Dec-2021
Abstract: 
The disruption on higher education across the globe through adverse events such as student
strikes, natural disasters and pandemics like Coronavirus Disease 2019 (Covid-19), can have
catastrophic long-term effects on its sustainability unless there are significant and innovative
research endeavours to mitigate this impact. Never before has the desire to keep learners
motivated, engaged and successful in advancing their knowledge and perfecting their 21st
century skills through student-centred, technology-rich teaching and learning practices,
become so imperative across disciplines and job profiles. In particular, the problem associated
with teaching programming to novice learners is further exacerbated by the complex and
abstract nature of the field and the heavy reliance on 21st century skills such as critical and
computational thinking. As a result, a kaleidoscope of research into programming self-efficacy,
the complexity of the field, teaching methods and a variety of teaching tools, have emerged
over the recent past. In response, the aim of this research was to use decision support systems
to obtain student-centred preferences for learning applications to promote critical thinking in
first year programming students. This study focuses on the visual programming environment
and critical thinking as the gateway skill for student success in understanding programming.
The extensive literature review has revealed an array of learning Apps and a multiplicity of
critical thinking criteria that serve a diverse set of needs and expectations. Therefore, research
to develop a multiple attribute decision-making model is needed to assist academics make
quick, scientifically-proven, accurate and collective decisions about which learning App to
choose from the range of available alternatives. The study used decision theory and Diane
Halpern’s 4-part model for critical thinking as the theoretical frameworks for evaluating and
selecting learning Apps on the basis of its capacity to promote critical thinking. As a
quantitative study, it randomly selected 217 students from a population of 500 programming
students to rate four learning Apps, namely, Scratch, Alice, Blockly and MIT App Inventor,
against critical thinking criteria and established Scratch as the App that best promotes critical
thinking among first year programming students. Consequently, its distinctiveness lies in its
use of the Fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Situation)
multi-criteria decision-making algorithm to rank criteria for critical thinking, calculate their
weights on the basis of informed opinion and hence scientifically deduce the best rated App
among the available alternatives that promote critical thinking among first year programming
students. Furthermore, the study offers useful, insightful and ranked critical thinking criteria to
formulate a user-friendly, transparent and evidence-based framework for App selection among
academics teaching programming in higher education institutions.
Description: 
Dissertation submitted in fulfillment of the requirement for the Master of Information and Communications Technology degree, Durban University of Technology, Durban, South Africa, 2021.
URI: https://hdl.handle.net/10321/4107
DOI: https://doi.org/10.51415/10321/4107
Appears in Collections:Theses and dissertations (Accounting and Informatics)

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