Intelligent decision support system for selection of Learning Apps to promote critical thinking in first year programming students

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.