Context-Aware Analysis of Group Submissions for Group Anomaly Detection and Performance Prediction

Source publication / research team or educational organization described in paper

Not specified in extracted text

Researchers, educators, instructors, or facilitators as described in the source publication

• Higher education

Course implementation or course design

Not specified in extracted text

bi-weekly quizzes. Data is collected from the course offered in Fall 2021 from 100 students assigned to groups of 10. Out of 100 stu- dents, 18% are identified as women, 81% identified as men and 1% identified a

Generative AI

• AI output reliability, hallucination, academic integrity, and age-appropriate use require safeguards.
• High-stakes or student-data-centered AI use requires stronger governance, transparency, and bias monitoring.

Higher education

Mixed or not explicitly specified; infer from target learner group and intervention design.

Varies by intervention; not specified unless the paper explicitly describes prerequisites.

• Document the AI education intervention, course, tool, or resource described in the source publication.
• Extract the learner context, AI role, pedagogy, outcomes, and constraints for AAB registry comparison.
• Learning exercises that activate students’ additional cognitive understanding of course concepts facilitate contextualizing the content knowledge and developing higher-order thinking and problem-solving skills.

• Support AAB comparison across AI literacy, AI education, teacher training, higher education, and workforce contexts.
• Capture evidence maturity, transferability, and limitations rather than treating the publication as product endorsement.

• Not an AAB endorsement of the tool, curriculum, provider, or result.
• Not a direct replication record unless the source paper reports implementation details sufficient for replication.

Generative AI

Not specified in extracted text

• Co-creator
• Evaluator

• Primary interaction pattern inferred from publication: Curriculum / course design.
• AI capability focus: Generative AI.

• Require human review of generated outputs and explicit guidance against over-reliance or answer copying.
• Minimize personal data collection and avoid storing identifiable learner media unless approved by local policy/IRB.

• Review the publication’s reported context, learner group, AI tool or curriculum, implementation process, and outcome evidence.
• Map the case to AAB registry fields for comparison across educational levels and AI capability types.
• Use the source publication and PDF for any manual verification before public registry release.

• Human educators/researchers remain responsible for instructional design, supervision, interpretation, and ethical safeguards.
• AI systems or AI concepts provide the learning object, support tool, evaluator, simulator, or automation context depending on the paper.

• Instructional / curriculum-based learning
• Registry extraction emphasizes explicit learning goals, observed outcomes, constraints, and safety limitations.

• AI output reliability, hallucination, academic integrity, and age-appropriate use require safeguards.
• High-stakes or student-data-centered AI use requires stronger governance, transparency, and bias monitoring.

• Case classified under: Published empirical study.
• Pedagogical pattern: Instructional / curriculum-based learning.
• Any additional adaptations should be verified against the full paper before public-facing publication.

• Engagement evidence should be interpreted according to the source paper’s reported method and sample.
• Student-generated instructional materials such as course summaries and problem sets are amongst the instructional strategies that reflect active learn- ing and constructivist philosophy.

• Student-generated instructional materials such as course summaries and problem sets are amongst the instructional strategies that reflect active learn- ing and constructivist philosophy.
• The contributions of this work are twofold: 1) We introduce a practical implementation of an inside-outside learning strat- egy in an undergraduate deep learning course and will share our experiences in incorporating student-generated instruc- tional materials learning strategy in course design, and
• This work opens up an avenue for effectively implementing a constructivist learning strategy in large-scale and online courses to build a sense of community between learners while providing an automated tool for instructors to identify at-risk groups.

Learning exercises that activate students’ additional cognitive understanding of course concepts facilitate contextualizing the content knowledge and developing higher-order thinking and problem-solving skills. Student-generated instructional materials such as course summaries and problem sets are amongst the instructional strategies that reflect active learn- ing and constructivist philosophy.

• Require human review of generated outputs and explicit guidance against over-reliance or answer copying.
• Minimize personal data collection and avoid storing identifiable learner media unless approved by local policy/IRB.

• Learning analytics
• Activity documentation

• Can be used as an AAB evidence record for cross-case comparison, standards drafting, and evidence-maturity mapping.
• Supports identification of recurring patterns in AI literacy, AI education implementation, teacher preparation, assessment, and responsible AI learning.

• Conceptual understanding
• Assessment / feedback quality
• Curriculum / course design
• Generative AI

• Completed

Higher education

Higher education

Generative AI

Instructional / curriculum-based learning

High

High

Context-Aware Analysis of Group Submissions for Group Anomaly Detection and Performance Prediction

• Narges Norouzi
• Amir Mazaheri

Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37 No. 13, EAAI-23

2023

10.1609/aaai.v37i13.26892

https://ojs.aaai.org/index.php/AAAI/article/view/26892

https://ojs.aaai.org/index.php/AAAI/article/view/26892/26664

083_Context-Aware Analysis of Group Submissions for Group Anomaly Detection and Performance Prediction.pdf

9

Learning exercises that activate students’ additional cognitive understanding of course concepts facilitate contextualizing the content knowledge and developing higher-order thinking and problem-solving skills. Student-generated instructional materials such as course summaries and problem sets are amongst the instructional strategies that reflect active learn- ing and constructivist philosophy. The contributions of this work are twofold: 1) We introduce a practical implementation of an inside-outside learning strat- egy in an undergraduate deep learning course and will share our experiences in incorporating student-generated instruc- tional materials learning strategy in course design, and 2) We develop a context-aware deep learning framework to draw in- sights from the student-generated materials for (i) Detecting anomalies in group activities and (ii) Predicting the median quiz performance of students in each group. This work opens up an avenue for effectively implementing a constructivist learning strategy in large-scale and online courses to build a sense of community between learners while providing an automated tool for instructors to identify at-risk groups.

• Higher education

• AI output reliability, hallucination, academic integrity, and age-appropriate use require safeguards.
• High-stakes or student-data-centered AI use requires stronger governance, transparency, and bias monitoring.

Not specified in extracted text unless noted in duration field.

Requires educators/researchers/facilitators with sufficient AI literacy and pedagogy knowledge for the target learners.

Infrastructure depends on AI tool type, learner devices, data access, and institutional policy context.

High

• duration

This entry was automatically extracted from the PDF text and manifest metadata. Fields should be manually verified before public registry publication, especially group size, location, duration, and outcome claims.