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Daniyaal Farooqi

Publications and source records attributed to Daniyaal Farooqi.

2 recordsLinked to original sources

Systematic Review of Academic Procrastination Interventions in Computing Higher Education

Academic procrastination is a persistent challenge in computing education, yet evidence on the effectiveness of course-level interventions remains fragmented across diverse designs and contexts. We present a systematic literature review of studies published in the past decade that empirically examine interventions to reduce academic procrastination among post-secondary computing students. Evidence from 19 articles examines interventions that target procrastination through structural, feedback-based, motivational, and self-regulatory mechanisms. Our findings suggest that interventions introducing clear temporal structure consistently promote earlier starts and more distributed work, which act as key mediators of performance gains. The magnitude of these gains depends strongly on task structure, with greater benefits for long-horizon, multi-step assignments than for short, routine tasks. Moreover, supportive designs reliably outperform punitive or restrictive schemes, while uniform interventions yield uneven benefits across students. This review highlights the importance of designing structured, supportive, and personalized interventions to address procrastination in computing education.

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Job Anxiety in Post-Secondary Computer Science Students Caused by Artificial Intelligence

The emerging widespread usage of AI has led to industry adoption to improve efficiency and increase earnings. However, a major consequence of this is AI displacing employees from their jobs, leading to feelings of job insecurity and uncertainty. This is especially true for computer science students preparing to enter the workforce. To investigate this, we performed semi-structured interviews with (n = 25) students across computer science undergraduate and graduate programs at the University of Toronto to determine the extent of job replacement anxiety. Through thematic analysis, it was determined that computer science students indeed face stress and anxiety from AI displacement of jobs, leading to different strategies of managing pressure. Subfields such as software engineering and web development are strongly believed to be vulnerable to displacement, while specialized subfields like quantum computing and AI research are deemed more secure. Many students feel compelled to upskill by using more AI technologies, taking AI courses, and specializing in AI through graduate school. Some students also reskill by pursuing other fields of study seen as less vulnerable to AI displacement. Finally, international students experience additional job replacement anxiety because of pressure to secure permanent residence. Implications of these findings include feelings of low security in computer science careers, oversaturation of computer science students pursuing AI, and potential dissuasion of future university students from pursuing computer science.

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