Sep 2026· international journal of engineering trends and technology· 0 citations
TL;DR
An open-source Learning Management System architecture supporting hybrid pre-laboratory instruction in a resource-constrained university setting is reported, together with a quantified account of its capacity envelope and its limits.
Abstract
This paper reports the design, implementation and deployment of an open-source Learning Management System (LMS) architecture supporting hybrid pre-laboratory instruction in a resource-constrained university setting. The platform was deployed with Tutor, the Docker-based distribution of Open edX, on a single virtual private server with four virtual CPU cores and 4 GB of RAM, with DNS and TLS resolution provided by Cloudflare. The architecture is layered across infrastructure, platform, course content and data processing. Two pre-laboratory modules were delivered through short instructional videos, auto-graded formative quizzes and an embedded interactive serious game, and the engineering trade-offs of third-party tool integration without a Learning Tools Interoperability bridge are analysed. A four-stage learning analytics pipeline extracts learner activity from the LMS gradebook, selects valid engagement signals and computes a composite engagement index. Operational validation over a continuous 58-day production period shows a mean edge response time of 67.3 ms (SD 14.0 ms, n = 20), an application-tier saturation throughput of approximately 74 requests per second, and zero failed requests across 1,600 load-test requests at concurrency levels from 5 to 50. Across the deployed cohort, 82.4% of learners in Module CS101 (n = 17) and 77.3% in Module CS102 (n = 22) attempted at least one formative quiz, and the composite engagement index (n = 17) reached a mean of 76.5% (SD 33.8%, median 88.9%). The result is a low-cost, reproducible blueprint for hybrid digital instruction, together with a quantified account of its capacity envelope and its limits.
Teaching programming at scale is hard when the tools for managing a classroom and the tools for grading code live on separate platforms. Most educators end up stitching together a general-purpose Learning Management System (LMS) like Moodle or Google Classroom with a standalone coding judge like HackerRank or LeetCode...
Sonali Patil, Supriya V. Mahadevkar, Mihir Pande et al.· International Conference on...· 0 citations
This study developed and evaluated Ansible-based cross-platform orchestration instructional media in an EVE-NG virtual laboratory for Computer and Network Engineering students at a vocational high school. The study employed the ADDIE development model, comprising analysis, design, development, implementation, and evalu...
Indar Bismoko, P. Sudira· Didaktika: Jurnal Kependidik...· 0 citations
This study developed a lightweight LINE-based mobile knowledge chatbot for the Level B Computer Hardware Repair certification subject and examined its instructional effectiveness in comparison with ChatGPT. The system was deployed through the LINE mobile messaging platform, allowing learners to access certification-rel...
Cheng-Hsiu Li· International Journal of Int...· 0 citations
The design pairs cognitive adaptivity, realised as mastery-based task selection under configurable parameters, with motivational design grounded in Keller’s ARCS model, and a formalised adaptation logic linking learner states, adaptive responses, and conjectures.
Lajos Toldi, Tünde Lengyel Molnár· Computers· 0 citations
Student researchers at small and mid-sized higher education institutions (HEIs) in the Global South wait days, sometimes longer, to secure a research adviser, get compliance documents verified, or receive statistical feedback. This paper documents the design, development, and pilot implementation of Project Feliann, an...
Tyrone Marcial V. Lopez, M. Viernes· VENTURE: A Faculty Multidisc...· 0 citations
LoopCAT is presented, an Apache-2.0-licensed, local-first computer-assisted translation environment co-created with OpenAI Codex using GPT-5.5 and GPT-5.6, and a framework connecting workflow competence, evaluative judgement, and technological agency is proposed.
Gökhan Doğru, Adrià Martín Mor· 0 citations
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.