Browsing by Author "Mathrani A"

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    Clustering Analysis on Sustainable Development Goal Indicators for Forty-Five Asian Countries
    (2023-03-28) Mathrani A; Wang J; Li D; Zhang X; Sacco, PL
    This paper draws upon the United Nations 2022 data report on the achievement of Sustainable Development Goals (SDGs) across the following four dimensions: economic, social, environmental and institutional. Ward’s method was applied to obtain clustering results for forty-five Asian countries to understand their level of progress and overall trends in achieving SDGs. We identified varying degrees of correlation between the four dimensions. The results show that East Asian countries performed poorly in the economic dimension, while some countries in Southeast Asia and Central and West Asia performed relatively well. Regarding social and institutional dimensions, the results indicate that East and Central Asian countries performed relatively better than others. Finally, in the environmental dimension, West and South Asian countries showed better performance than other Asian countries. The insights gathered from this study can inform policymakers of these countries about their own country’s position in achieving SDGs in relation to other Asian countries, as they work towards establishing strategies for improving their sustainable development targets.
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    Data Quality Challenges in Educational Process Mining: Building Process-Oriented Event Logs from Process-Unaware Online Learning Systems
    (Inderscience, 2022-05-04) Umer R; Susnjak T; Mathrani A; Suriadi S
    Educational process mining utilizes process-oriented event logs to enable discovery of learning practices that can be used for the learner’s advantage. However, learning platforms are often process-unaware, therefore do not accurately reflect ongoing learner interactions. We demonstrate how contextually relevant process models can be constructed from process-unaware systems. Using a popular learning management system (Moodle), we have extracted stand-alone activities from the underlying database and formatted it to link the learners’ data explicitly to process instances (cases). With a running example that describes quiz-taking activities undertaken by students, we describe how learner interactions can be captured to build process-oriented event logs. This article contributes to the fields of learning analytics and education process mining by providing lessons learned on the extraction and conversion of process-unaware data to event logs for the purpose of analysing online education data.
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    Data Quality Challenges in Educational Process Mining: Building Process-Oriented Event Logs from Process-Unaware Online Learning Systems
    (Inderscience, 2022-05-16) Umer R; Susnjak T; Mathrani A; Suriadi S
    Educational process mining utilizes process-oriented event logs to enable discovery of learning practices that can be used for the learner’s advantage. However, learning platforms are often process-unaware, therefore do not accurately reflect ongoing learner interactions. We demonstrate how contextually relevant process models can be constructed from process-unaware systems. Using a popular learning management system (Moodle), we have extracted stand-alone activities from the underlying database and formatted it to link the learners’ data explicitly to process instances (cases). With a running example that describes quiz-taking activities undertaken by students, we describe how learner interactions can be captured to build process-oriented event logs. This article contributes to the fields of learning analytics and education process mining by providing lessons learned on the extraction and conversion of process-unaware data to event logs for the purpose of analysing online education data.
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    Digital divide framework: online learning in developing countries during the COVID-19 lockdown
    (Taylor and Francis Group, 2022) Mathrani A; Sarvesh T; Umer R
    This article showcases digital inequalities that came to the forefront for online learning during the COVID-19 lockdown across five developing countries, India, Pakistan, Bangladesh, Nepal and Afghanistan. Large sections of population in developing economies have limited access to basic digital services; this, in turn, restricts how digital media are being used in everyday lives. A digital divide framework encompassing three analytical perspectives, structure, cultural practices and agency, has been developed. Each perspective is influenced by five constructs, communities, time, location, social context and sites of practice. Community relates to gendered expectations, time refers to the lockdown period while locations are interleaved online classrooms and home spaces. Societal contexts influence aspects of online learning and how students engage within practice sites. We find structural issues are due to lack of digital media access and supporting services; further that female students are more often placed lower in the digital divide access scale. Cultural practices indicate gendered discriminatory rules, with female students reporting more stress due to added household responsibilities. This impacts learner agency and poses challenges for students in meaningfully maximising their learning outcomes. Our framework can inform policy-makers to plan initiatives for bridging digital divide and set up equitable gendered learning policies.
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    Exogenous and endogenous knowledge structures in dual-mode course deliveries
    (Elsevier Ltd, 2020-12-05) Mathrani S; Mathrani A; Khatun M
    Learning Management Systems (LMS) have facilitated any-time any-place learning; thereby enabling educational institutions to embrace dual-mode teaching by engaging with on- and off-campus student cohorts. This study examines how lecturing staff build on existing knowledge structures when tasked with delivery of the same course concurrently over dual modes. Exogeneous knowledge structures are constructed from one's environment, while endogenous structures are rooted in how lecturing staff assimilate their teaching and learning environments to meet the needs of dual-mode learners. Activity Theory provided the theoretical lens and has revealed how exogeneous structures are influenced by the outer triangle elements, that in turn inform on interactions occurring among the inner triangle elements to construct endogenous knowledge. Seventeen lecturing staff participated via open-ended surveys to share their pedagogic approaches for maintaining equable learning experiences across both student cohorts. Findings show that staff can be constrained by LMS functionalities, course curriculum, course structure and lack of technical support. Staff employed additional online tools, changed assessment methods and encouraged online discussions to bring more parity across both learner groups. Our study provides new insights on dual-mode teaching deliveries and shares how lecturing staff develop new knowledge structures from their teaching practice. It will further help in development of instructional strategies especially post Covid-19, when it is likely that teaching modes will have more online component compared to current times.
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    Fat stigma and body objectification: A text analysis approach using social media content
    (SAGE Publications, 2022-08-15) Wanniarachchi V; Scogings C; Susnjak T; Mathrani A
    This study investigates how female and male genders are positioned in fat stigmatising discourses that are being conducted over social media. Weight-based linguistic data corpus, extracted from three popular social media (SM) outlets, Twitter, YouTube and Reddit, was examined for fat stigmatising content. A mixed-method analysis comprising sentiment analysis, word co-occurrences and qualitative analysis, assisted our investigation of the corpus for body objectification themes and gender-based differences. Objectification theory provided the underlying framework to examine the experiential consequences of being fat across both genders. Five objectifying themes, namely, attractiveness, physical appearance, lifestyle choices, health and psychological well-being, emerged from the analysis. A deeper investigation into more facets of the social interaction data revealed overall positive and negative attitudes towards obesity, which informed on existing notions of gendered body objectification and weight/fat stigmatisation. Our findings have provided a holistic outlook on weight/fat stigmatising content that is posted online which can further inform policymakers in planning suitable props to facilitate more inclusive SM spaces. This study showcases how lexical analytics can be conducted by combining a variety of data mining methods to draw out insightful subject-related themes that add to the existing knowledge base; therefore, has both practical and theoretical implications.
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    Gender Diversity Population Simulations in an Extended Game of Life Context
    (IEEE, 2019-06-20) Mathrani A; Scogings C; Mathrani S
    Cellular automata studies have been instrumental in computational and biological studies for simulating life contours based on simple rule-based strategies. Game of Life (GoL) presented us with one of the earliest automata studies that led the way in exemplifying non-linear spatial representations, such as large-scale population evolution scenarios depicting species dominance, species equilibrium, and species extinction. However, the GoL was driven by interactions among vegetative entities comprising live and die states only. This paper extends GoL to gendered-GoL (g-GoL) in which male phenotypes and female phenotypes interact in an extended world to procreate. Using the g-GoL, we have demonstrated many evolution contours by applying gender-based dependence rules. Evolution scenarios have been simulated with skewed gender ratios that favor the birth of male offspring. Preference for a male child is common in certain cultures; therefore, empirical data realized with skewed gender settings in g-GoL can reveal the long-term impact of non-egalitarian gender societal structures. Our model provides a tool for the study of emergent life contours and brings awareness on current gender imbalances to strengthen multi-disciplinary research inquiry in the areas of social practices, mathematical modeling, and use of computational technologies.
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    Hate Speech Patterns in Social Media: A Methodological Framework and Fat Stigma Investigation Incorporating Sentiment Analysis, Topic Modelling and Discourse Analysis
    (Australasian Association for Information Systems and Australian Computer Society, 2023-02-08) Wanniarachchi V; Scogings C; Susnjak T; Mathrani A
    Social media offers users an online platform to freely express themselves; however, when users post opinionated and offensive comments that target certain individuals or communities, this could instigate animosity towards them. Widespread condemnation of obesity (fatness) has led to much fat stigmatizing content being posted online. A methodological framework that uses a novel mixed-method approach for unearthing hate speech patterns from large text-based corpora gathered from social media is proposed. We explain the use of computer-mediated quantitative methods comprising natural language processing techniques such as sentiment analysis, emotion analysis and topic modelling, along with qualitative discourse analysis. Next, we have applied the framework to a corpus of texts on gendered and weight-based data that have been extracted from Twitter and Reddit. This assisted in the detection of different emotions being expressed, the composition of word frequency patterns and the broader fat-based themes underpinning the hateful content posted online. The framework has provided a synthesis of quantitative and qualitative methods that draw on social science and data mining techniques to build real-world knowledge in hate speech detection. Current information systems research is limited in its use of mixed analytic approaches for studying hate speech in social media. Our study therefore contributes to future research by establishing a roadmap for conducting mixed-method analyses for better comprehension and understanding of hate speech patterns.
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    Interpreting academic integrity transgressions among learning communities
    (BioMed Central Limited, 2021-12) Mathrani A; Han B; Mathrani S; Jha M; Scogings C
    Educational institutions rely on academic citizenship behaviors to construct knowledge in a responsible manner. However, they often struggle to contain the unlawful reuse of knowledge (or academic citizenship transgressions) by some learning communities. This study draws upon secondary data from two televised episodes describing contract cheating (or ghostwriting) practices prevalent among international student communities. Against this background, we have investigated emergent teaching and learning structures that have been extended to formal and informal spaces with the use of mediating technologies. Learners’ interactions in formal spaces are influenced by ongoing informal social experiences within a shared cultural context to influence learners’ agency. Building upon existing theories, we have developed an analytical lens to understand the rationale behind cheating behaviors. Citizenship behaviors are based on individual and collective perceptions of what constitutes as acceptable or unacceptable behavior. That is, learners who are low in motivation and are less engaged with learning may collude; more so, if cheating is not condemned by members belonging to their informal social spaces. Our analytical lens describes institutional, cultural, technological, social and behavioral contexts that influence learner agency.
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    Learning analytics dashboard: a tool for providing actionable insights to learners
    (Springer Open, 2022-02-14) Susnjak T; Ramaswami G; Mathrani A
    This study investigates current approaches to learning analytics (LA) dashboarding while highlighting challenges faced by education providers in their operationalization. We analyze recent dashboards for their ability to provide actionable insights which promote informed responses by learners in making adjustments to their learning habits. Our study finds that most LA dashboards merely employ surface-level descriptive analytics, while only few go beyond and use predictive analytics. In response to the identified gaps in recently published dashboards, we propose a state-of-the-art dashboard that not only leverages descriptive analytics components, but also integrates machine learning in a way that enables both predictive and prescriptive analytics. We demonstrate how emerging analytics tools can be used in order to enable learners to adequately interpret the predictive model behavior, and more specifically to understand how a predictive model arrives at a given prediction. We highlight how these capabilities build trust and satisfy emerging regulatory requirements surrounding predictive analytics. Additionally, we show how data-driven prescriptive analytics can be deployed within dashboards in order to provide concrete advice to the learners, and thereby increase the likelihood of triggering behavioral changes. Our proposed dashboard is the first of its kind in terms of breadth of analytics that it integrates, and is currently deployed for trials at a higher education institution.
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    Learning analytics dashboard: a tool for providing actionable insights to learners
    (BioMed Central Ltd, 2022-12) Susnjak T; Ramaswami G; Mathrani A
    This study investigates current approaches to learning analytics (LA) dashboarding while highlighting challenges faced by education providers in their operationalization. We analyze recent dashboards for their ability to provide actionable insights which promote informed responses by learners in making adjustments to their learning habits. Our study finds that most LA dashboards merely employ surface-level descriptive analytics, while only few go beyond and use predictive analytics. In response to the identified gaps in recently published dashboards, we propose a state-of-the-art dashboard that not only leverages descriptive analytics components, but also integrates machine learning in a way that enables both predictive and prescriptive analytics. We demonstrate how emerging analytics tools can be used in order to enable learners to adequately interpret the predictive model behavior, and more specifically to understand how a predictive model arrives at a given prediction. We highlight how these capabilities build trust and satisfy emerging regulatory requirements surrounding predictive analytics. Additionally, we show how data-driven prescriptive analytics can be deployed within dashboards in order to provide concrete advice to the learners, and thereby increase the likelihood of triggering behavioral changes. Our proposed dashboard is the first of its kind in terms of breadth of analytics that it integrates, and is currently deployed for trials at a higher education institution.
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    Methodological Aspects in Study of Fat Stigma in Social Media Contexts: A Systematic Literature Review
    (MDPI (Basel, Switzerland), 2022-05-17) Wanniarachchi VU; Mathrani A; Susnjak T; Scogings C; Moreno, A
    With increased obesity rates worldwide and the rising popularity in social media usage, we have witnessed a growth in hate speech towards fat/obese people. The severity of hate content has prompted researchers to study public perceptions that give rise to fat stigma from social media discourses. This article presents a systematic literature review of recent literature published in this domain to gauge the current state of research and identify possible research gaps. We have examined existing research (i.e., peer-reviewed articles that were systematically included using the EBSCO discovery service) to study their methodological aspects by reviewing their context, domain, analytical methods, techniques, tools, features and limitations. Our findings reveal that while recent studies have explored fat stigma content in social media, these mostly acquired manual analytical methods regardless of the evolved machine learning, natural language processing and deep learning methods. Although fat stigma in social media has gained enormous attention in current socio-psychological research, there exists a gap between how such research is conducted and what technologies are being applied, which limits in-depth investigations of fat stigma discussions.
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    Methodological Aspects in Study of Fat Stigma in Social Media Contexts: A Systematic Literature Review
    (MDPI, Basel, Switzerland., 2022-05-17) Wanniarachchi V; Mathrani A; Susnjak T; Scogings C; Moreno, A
    With increased obesity rates worldwide and the rising popularity in social media usage, we have witnessed a growth in hate speech towards fat/obese people. The severity of hate content has prompted researchers to study public perceptions that give rise to fat stigma from social media discourses. This article presents a systematic literature review of recent literature published in this domain to gauge the current state of research and identify possible research gaps. We have examined existing research (i.e., peer-reviewed articles that were systematically included using the EBSCO discovery service) to study their methodological aspects by reviewing their context, domain, analytical methods, techniques, tools, features and limitations. Our findings reveal that while recent studies have explored fat stigma content in social media, these mostly acquired manual analytical methods regardless of the evolved machine learning, natural language processing and deep learning methods. Although fat stigma in social media has gained enormous attention in current socio-psychological research, there exists a gap between how such research is conducted and what technologies are being applied, which limits in-depth investigations of fat stigma discussions.
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    On Developing Generic Models for Predicting Student Outcomes in Educational Data Mining
    (MDPI, 2022-01-07) Ramaswami G; Susnjak T; Mathrani A; Cowling, M; Jha, M
    Poor academic performance of students is a concern in the educational sector, especially if it leads to students being unable to meet minimum course requirements. However, with timely prediction of students’ performance, educators can detect at-risk students, thereby enabling early interventions for supporting these students in overcoming their learning difficulties. However, the majority of studies have taken the approach of developing individual models that target a single course while developing prediction models. These models are tailored to specific attributes of each course amongst a very diverse set of possibilities. While this approach can yield accurate models in some instances, this strategy is associated with limitations. In many cases, overfitting can take place when course data is small or when new courses are devised. Additionally, maintaining a large suite of models per course is a significant overhead. This issue can be tackled by developing a generic and course-agnostic predictive model that captures more abstract patterns and is able to operate across all courses, irrespective of their differences. This study demonstrates how a generic predictive model can be developed that identifies at-risk students across a wide variety of courses. Experiments were conducted using a range of algorithms, with the generic model producing an effective accuracy. The findings showed that the CatBoost algorithm performed the best on our dataset across the F-measure, ROC (receiver operating characteristic) curve and AUC scores; therefore, it is an excellent candidate algorithm for providing solutions on this domain given its capabilities to seamlessly handle categorical and missing data, which is frequently a feature in educational datasets.
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    On Developing Generic Models for Predicting Student Outcomes in Educational Data Mining
    (MDPI (Basel, Switzerland), 2022-03) Ramaswami G; Susnjak T; Mathrani A; Cowling, M; Jha, M
    Poor academic performance of students is a concern in the educational sector, especially if it leads to students being unable to meet minimum course requirements. However, with timely prediction of students’ performance, educators can detect at-risk students, thereby enabling early interventions for supporting these students in overcoming their learning difficulties. However, the majority of studies have taken the approach of developing individual models that target a single course while developing prediction models. These models are tailored to specific attributes of each course amongst a very diverse set of possibilities. While this approach can yield accurate models in some instances, this strategy is associated with limitations. In many cases, overfitting can take place when course data is small or when new courses are devised. Additionally, maintaining a large suite of models per course is a significant overhead. This issue can be tackled by developing a generic and course-agnostic predictive model that captures more abstract patterns and is able to operate across all courses, irrespective of their differences. This study demonstrates how a generic predictive model can be developed that identifies at-risk students across a wide variety of courses. Experiments were conducted using a range of algorithms, with the generic model producing an effective accuracy. The findings showed that the CatBoost algorithm performed the best on our dataset across the F-measure, ROC (receiver operating characteristic) curve and AUC scores; therefore, it is an excellent candidate algorithm for providing solutions on this domain given its capabilities to seamlessly handle categorical and missing data, which is frequently a feature in educational datasets.
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    Online Tracking: When Does it Become Stalking?
    (World Scientific, 2021-05-25) Amarasekara B; Mathrani A; Scogings C
    Online user activities are tracked for many purposes. In e-commerce, cross-domain tracking is used to quantify and pay for web-tra±c generation. Our previous research studies have shown that HTTP cookie-based tracking process, though reliable, can fail due to technical reasons, as well as through fraudulent manipulation by tra±c generators. In this research study, we evaluate which of the previously published tracking mechanisms are still functional. We assess the e±cacy and utility of those methods to create a robust tracking mechanism for e-commerce. A failsafe and robust tracking mechanism does not need to translate into further privacy intrusions. Many countries are rushing to introduce new regulations, which can have a negative impact on the development of robust technologies in an inherently stateless eco-system. We used a multi-domain, purpose-built simulation environment to experiment common tracking scenarios, and to describe the parameters that de¯ne the minimum tracking requirement use-cases, and practices that result in invading privacy of users. This study will help practitioners in their implementations, and policy developers and regulators to draw up policies that would not curtail the development of robust tracking technologies that are needed in e-commerce activities, while safeguarding the privacy of internet users.
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    Online Tracking: When Does it Become Stalking?
    (World Scientific Publishing, 2021-11) Amarasekara BR; Mathrani A; Scogings C
    Online user activities are tracked for many purposes. In e-commerce, cross-domain tracking is used to quantify and pay for web-tra±c generation. Our previous research studies have shown that HTTP cookie-based tracking process, though reliable, can fail due to technical reasons, as well as through fraudulent manipulation by tra±c generators. In this research study, we evaluate which of the previously published tracking mechanisms are still functional. We assess the e±cacy and utility of those methods to create a robust tracking mechanism for e-commerce. A failsafe and robust tracking mechanism does not need to translate into further privacy intrusions. Many countries are rushing to introduce new regulations, which can have a negative impact on the development of robust technologies in an inherently stateless eco-system. We used a multi-domain, purpose-built simulation environment to experiment common tracking scenarios, and to describe the parameters that de¯ne the minimum tracking requirement use-cases, and practices that result in invading privacy of users. This study will help practitioners in their implementations, and policy developers and regulators to draw up policies that would not curtail the development of robust tracking technologies that are needed in e-commerce activities, while safeguarding the privacy of internet users.
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    Perspectives on the challenges of generalizability, transparency and ethics in predictive learning analytics
    (Elsevier Ltd., 2021-11-20) Mathrani A; Susnjak T; Ramaswami G; Barczak A
    Educational institutions need to formulate a well-established data-driven plan to get long-term value from their learning analytics (LA) strategy. By tracking learners’ digital traces and measuring learners’ performance, institutions can discern consequential learning trends via use of predictive models to enhance their instructional services. However, questions remain on how the proposed LA system is suitable, meaningful, and justifiable. In this concept paper, we examine generalizability and transparency of the internals of predictive models, alongside the ethical challenges in using learners’ data for building predictive capabilities. Model generalizability or transferability is hindered by inadequate feature representation, small and imbalanced datasets, concept drift, and contextually un-related domains. Additional challenges relate to trustworthiness and social acceptance of these models since algorithmic-driven models are difficult to interpret by themselves. Further, ethical dilemmas are faced in engaging with learners’ data while developing and deploying LA systems at an institutional level. We propose methodologies for apprehending these challenges by establishing efforts for managing transferability and transparency, and further assessing the ethical standing on justifiable use of the LA strategy. This study showcases underlying relationships that exist between constructs pertaining to learners’ data and the predictive model. We suggest the use of appropriate evaluation techniques and setting up research ethics protocols, since without proper controls in place, the model outcome would not be portable, transferable, trustworthy, or admissible as a responsible outcome. This concept paper has theoretical and practical implications for future inquiry in the burgeoning field of learning analytics.
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    Perspectives on the challenges of generalizability, transparency and ethics in predictive learning analytics
    (Elsevier Ltd, 2021-12-01) Mathrani A; Susnjak T; Ramaswami G; Barczak A
    Educational institutions need to formulate a well-established data-driven plan to get long-term value from their learning analytics (LA) strategy. By tracking learners’ digital traces and measuring learners’ performance, institutions can discern consequential learning trends via use of predictive models to enhance their instructional services. However, questions remain on how the proposed LA system is suitable, meaningful, and justifiable. In this concept paper, we examine generalizability and transparency of the internals of predictive models, alongside the ethical challenges in using learners’ data for building predictive capabilities. Model generalizability or transferability is hindered by inadequate feature representation, small and imbalanced datasets, concept drift, and contextually un-related domains. Additional challenges relate to trustworthiness and social acceptance of these models since algorithmic-driven models are difficult to interpret by themselves. Further, ethical dilemmas are faced in engaging with learners’ data while developing and deploying LA systems at an institutional level. We propose methodologies for apprehending these challenges by establishing efforts for managing transferability and transparency, and further assessing the ethical standing on justifiable use of the LA strategy. This study showcases underlying relationships that exist between constructs pertaining to learners’ data and the predictive model. We suggest the use of appropriate evaluation techniques and setting up research ethics protocols, since without proper controls in place, the model outcome would not be portable, transferable, trustworthy, or admissible as a responsible outcome. This concept paper has theoretical and practical implications for future inquiry in the burgeoning field of learning analytics.
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    Rural–Urban, Gender, and Digital Divides during the COVID-19 Lockdown: A Multi-Layered Study
    (MDPI AG, 2023-05-09) Mathrani A; Umer R; Sarvesh T; Adhikari J
    This study explores digital divide issues that influenced online learning activities during the COVID-19 lockdown in five developing countries in South Asia. A multi-layered and interpretive analytical lens guided by three interrelated perspectives—structure, cultural practices, and agency—revealed various nuanced aspects across location-based (i.e., rural vs. urban) and across gendered (i.e., male vs. female) student groups. A key message that emerged from our investigation was the subtle ways in which the digital divide is experienced, specifically by female students and by students from rural backgrounds. Female students face more structural and cultural impositions than male students, which restricts them from fully availing digital learning opportunities. Rich empirical evidence shows these impositions are further exacerbated at times of crisis, leading to a lack of learning (agency) for women. This research has provided a gendered and regional outlook on digital discriminations and other inequalities that came to the forefront during the COVID-19 lockdown. This study is especially relevant as online learning is being touted as the next step in digitization; therefore, it can inform educational policymaking and help build inclusive digital societies and bridge current gender and regional divisions.