AI-Powered Interactive Learning Analytics Platform for Computer Education

Project Abstract

AI-Powered Interactive Learning Analytics Platform for Computer Education investigates how advanced analytics, adaptive feedback, and interactive visualization can transform computer science pedagogy by enabling data-driven instruction, personalized learning paths, and scalable assessment in higher education. This study builds a unified platform that ingests diverse educational data, including student interactions with coding environments, LMS activity, assessment results, and peer collaboration metrics, to generate actionable insights for instructors, instructional designers, and learners. The core objective is to design, implement, and evaluate an end-to-end system that (1) collects and harmonizes heterogeneous data sources in real time, (2) applies explainable machine learning models to identify at-risk students, skill mastery gaps, and effective learning sequences, (3) delivers personalized, scenario-based learning recommendations and adaptive content, and (4) provides intuitive visual analytics dashboards for monitoring progress and informing instructional decisions. The platform leverages a modular architecture consisting of a data ingestion and preprocessing layer, a analytics engine with interpretability-focused models, a recommendation and feedback module, and an interactive visualization layer integrated with a user-friendly learner dashboard. It enables instructors to configure competency frameworks, define learning objectives, and align analytics with assessment rubrics. The research emphasizes privacy-preserving data practices, robust access controls, and compliance with relevant educational data standards. A mixed-methods evaluation is conducted across multiple computer education courses to assess system usability, perceived usefulness, learning gains, and instructional impact. Quantitative evaluation employs controlled experiments and quasi-experimental designs to compare cohorts receiving analytics-informed interventions against traditional instruction, measuring outcomes such as concept mastery, programming proficiency, time-to-competence, and persistence. Statistical techniques include multilevel modeling, propensity score matching, and causal inference methods to ascertain the effect sizes of recommended interventions. Qualitative insights are gathered through educator interviews, focus groups, and artifact analysis to understand adoption challenges, interpretability, and workflow integration within existing teaching practices. Key contributions include (i) a scalable data fusion framework that supports real-time analytics from coding environments, LMS logs, and assessment platforms; (ii) an interpretable predictive analytics suite that identifies learners at risk and recommends targeted micro-activities, practice tasks, and remedial resources; (iii) adaptive learning sequences that personalize problem sets, project scaffolding, and feedback timing to optimize student engagement and mastery; (iv) visualization dashboards that translate complex analytics into decision-ready insights with explainable AI components; and (v) guidelines for ethical data governance, inclusive design, and educator professional development. The anticipated outcomes demonstrate improved learner engagement, higher achievement in computational problem-solving, and enhanced teaching efficacy through data-informed pedagogy. The research contributes to the discourse on intelligent educational technologies by presenting a practical, scalable platform that harmonizes learning analytics with actionable instructional support within the computer education domain. Limitations include variability in data quality across institutions, the need for ongoing model recalibration to evolving curricula, and potential resistance to analytics-driven instruction, which are addressed through iterative design, stakeholder engagement, and comprehensive training modules.

Project Overview

What This Project Is About

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A straightforward exploration of how data from learning activities in computer education can be analyzed to support better teaching and student learning. The project builds a system that collects basic learning data, turns it into useful insights, and presents feedback to teachers and students in plain terms.

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The Problem It Addresses

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Many courses in computer education rely on instructor judgment rather than concrete data about student progress. This can miss early signs of struggle or misaligned teaching methods. The project aims to provide timely, easy-to-understand analytics that help instructors adjust content and support students who are falling behind.

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Objectives of the Project

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1. Identify key learning activities and performance indicators in computer education courses.
2. Design an analytics dashboard that presents simple, actionable insights for instructors.
3. Prototype a feedback mechanism that guides student study plans.
4. Evaluate how analytics influence teaching decisions and student outcomes.

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What You Will Do Step by Step

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1. Review existing teaching challenges in computer education. 2. Collect anonymized data from course activities (e.g., quizzes, coding tasks, participation). 3. Build a lightweight data model to summarize progress. 4. Create a user-friendly dashboard for teachers and a learn-plan feature for students. 5. Run a small pilot test with one course and gather feedback. 6. Analyze how insights correlate with student performance.

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Expected Outcome

An accessible analytics tool that helps teachers identify at-risk students, adapt teaching methods, and support students in creating personalized study plans. The project should demonstrate improved awareness of learning gaps and potential improvements in course outcomes.