Browsing by Author "Umer R"

Now showing 1 - 5 of 5
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    All-Dielectric Transreflective Angle-Insensitive Near-Infrared (NIR) Filter
    (MDPI (Basel, Switzerland), 2022-08) Shaukat A; Umer R; Noble F; Arif K
    This paper presents an all-dielectric, cascaded, multilayered, thin-film filter, allowing near-infrared filtration for spectral imaging applications. The proposed design is comprised of only eight layers of amorphous silicon (A-Si) and silicon nitride (Si₃N₄), successively deposited on a glass substrate. The finite difference time domain (FDTD) simulation results demonstrate a distinct peak in the near-infrared (NIR) region with transmission efficiency up to 70% and a full-width-at-half-maximum (FWHM) of 77 nm. The theoretical results are angle-insensitive up to 60⁰ and show polarization insensitivity in the transverse magnetic (TM) and transverse electric (TE) modes. The theoretical response, obtained with the help of spectroscopic ellipsometry (SE), is in good agreement with the experimental result. Likewise, the experimental results for polarization insensitivity and angle invariance of the thin films are in unison with the theoretical results, having an angle invariance up to 50⁰.
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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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    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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    Rural–Urban, Gender, and Digital Divides during the COVID-19 Lockdown: A Multi-Layered Study
    (MDPI AG, 9/05/2023) 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.
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    Use of Predictive Analytics within Learning Analytics Dashboards: A Review of Case Studies
    (Springer Nature BV, 2023-09-01) Ramaswami G; Susnjak T; Mathrani A; Umer R
    Learning analytics dashboards (LADs) provide educators and students with a comprehensive snapshot of the learning domain. Visualizations showcasing student learning behavioral patterns can help students gain greater self-awareness of their learning progression, and at the same time assist educators in identifying those students who may be facing learning difficulties. While LADs have gained popularity, existing LADs are still far behind when it comes to employing predictive analytics into their designs. Our systematic literature review has revealed limitations in the utilization of predictive analytics tools among existing LADs. We find that studies leveraging predictive analytics only go as far as identifying the at-risk students and do not employ model interpretation or explainability capabilities. This limits the ability of LADs to offer data-driven prescriptive advice to students that can offer them guidance on appropriate learning adjustments. Further, published studies have mostly described LADs that are still at prototype stages; hence, robust evaluations of how LADs affect student outcomes have not yet been conducted. The evaluations until now are limited to LAD functionalities and usability rather than their effectiveness as a pedagogical treatment. We conclude by making recommendations for the design of advanced dashboards that more fully take advantage of machine learning technologies, while using suitable visualizations to project only relevant information. Finally, we stress the importance of developing dashboards that are ultimately evaluated for their effectiveness.