Andy Khong, Predictive Analytics and AI for Detecting Students Struggling Academically in Higher Ed.

Value of the Information & Strength of the Argument

The talk provides valuable insights into the application of AI and digital phenotyping in higher education, a relatively novel area. The speaker presents concrete data from an ongoing study, offering a realistic view of the challenges and opportunities. The argumentation is solid, grounded in established psychometric instruments and a clear methodology. However, the findings are preliminary and not yet peer-reviewed, and the speaker acknowledges limitations such as the lack of clustering analysis and the need for qualitative context. The discussion with the audience adds depth, raising important questions about individual trajectories and the influence of academic workload.

Scientific Rigor, Source Quality, Title Accuracy

The presentation demonstrates scientific rigor through the use of validated instruments (PHQ-9, GAD-7, etc.) and an IRB-approved study design. The speaker transparently discusses limitations and future steps. The title accurately reflects the content, focusing on predictive analytics and AI for detecting struggling students. No external sources are cited in the talk or description, so the quality of sources is based on the methodology presented. The audience comments (not provided) would be needed to assess public reception.

The title accurately reflects the content: the talk focuses on using predictive analytics and AI to detect students at risk of academic struggles, with emphasis on mental health and well-being.

7/10

The talk presents preliminary findings from an ongoing study, with transparent discussion of limitations and unanswered questions. The methodology is sound (validated instruments, IRB approval), but results are not yet published in peer-reviewed venues, and the speaker acknowledges the need for further analysis (e.g., clustering, focus groups).

Markers derived by PSI from the transcript: the creator did not define chapters.

• 02:00Introduction to the speaker and the context of AI in education.
• 05:00Overview of mental health statistics in Singapore and the motivation for the research.
• 10:00Explanation of digital phenotyping and its two categories.
• 15:00Introduction to Project Rightline: study design, participants, and data collection.
• 20:00Presentation of psychometric instruments used (PHQ-9, GAD-7, etc.).
• 25:00Discussion of preliminary results on anxiety and depression trajectories.
• 30:00Analysis of passive data: sleep, steps, and screen time patterns.
• 35:00Presentation of GPS regularity scores and differences between on-campus and off-campus students.
• 40:00Q&A session: discussion on clustering, individual trajectories, and future directions.