PL Framework Guideline for ODL [Webtoolkit]

These tools enable the creation of personalised content and interactive materials that support personalisation. They allow designers to tailor courses, materials, and the learning process to the unique needs of different learners.

Examples of the authoring tools include:

• Articulate storyline 360
• Adobe Captivate
• 360 learning
• Camtasia
• Elucidat
• Easygenerator
• Genially
• H5P
• Ispring
• Lectora
• Mindsmith

Resources

• 9 Authoring Tool Examples Training Providers Should Know About in 2026
• 10 Authoring tool examples

References

• Spivakovsky, O., Omelchuk, S., Malchykova, D., & Lemeshchuk, O. (2025, September). Institutional Digital Tools for Designing Student’s Personalized Learning Paths. In International Conference on Information and Communication Technologies in Education, Research, and Industrial Applications (pp. 211-223). Cham: Springer Nature Switzerland.
• Bontchev, B., & Vassileva, D. (2009, April). Courseware authoring for adaptive e-learning. In 2009 International Conference on Education Technology and Computer (pp. 176-180). IEEE.
• Vassileva, D., Bontchev, B., Chavkova, B., & Mitev, V. (2009, September). Software construction of an authoring tool for adaptive e-learning platforms. In 2009 Fourth Balkan Conference in Informatics (pp. 187-192). IEEE.
• Asgari, S., & Mehrpouyan, A. (2018). A group comparison of E-learning authoring tools in educational production and management. Indian Journal of Science and Technology, 11(24), 1-6.

AI provides the capability to utilise existing data in order to offer personalised experiences. It uses algorithms that enable machines to learn from data, automatically identify patterns, and apply those patterns for prediction and decision-making. Methods such as web scraping allow for the automated extraction of data and resources from websites and online platforms such as Facebook, Twitter etc, which can then be used to create personalised learning materials. In addition, the availability of AI models and APIs facilitates the efficient development of web scraping methods to offer tailored educational content. Other technologies involved include the use of Python libraries for web scraping, JavaScript, and related tools.

Resources

• Using Artificial Intelligence to Optimize Personalized Learning
• 39 Examples of Artificial Intelligence in Education
• How the Education Industry Benefits from Web scraping

References

• Fortuna, A., Prasetya, F., Samala, A. D., Rawas, S., Criollo-C, S., Kaya, D., ... & Nabawi, R. A. (2025). Artificial intelligence in personalized learning: A global systematic review of current advancements and shaping future opportunities. Social Sciences & Humanities Open, 12, 102114.
• Gligorea, I., Cioca, M., Oancea, R., Gorski, A. T., Gorski, H., & Tudorache, P. (2023). Adaptive learning using artificial intelligence in e-learning: A literature review. Education Sciences, 13(12), 1216.
• Karthikeyan, T., Sekaran, K., Ranjith, D., & Balajee, J. M. (2019). Personalized content extraction and text classification using effective web scraping techniques. International Journal of Web Portals (IJWP), 11(2), 41–52.
• Mimoudi, A. (2024). AI, personalized education, and challenges. In Proceedings of the International Conference on AI Research. Academic Conferences and Publishing Limited.
• Sakarkar, S., Chaudhari, V., Gaurkar, T., Veer, A., & SCET, M. K. (2021). Web personalisation based on user interaction. In 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) (pp. 234–238). IEEE.
• Tavakoli, M., Faraji, A., Molavi, A., Mol, S. T., & Kismihók, G. (2022). Hybrid human-AI curriculum development for personalised informal learning environments. In LAK22: 12th International Learning Analytics and Knowledge Conference (pp. 563–569).
• Villegas-Ch, W., & Luján-Mora, S. (2017). Analysis of data mining techniques applied to LMS for personalized education. In 2017 IEEE World Engineering Education Conference (EDUNINE) (pp. 85–89). IEEE.
• Wu, S., Cao, Y., Cui, J., Li, R., Qian, H., Jiang, B., & Zhang, W. (2024). A comprehensive exploration of personalized learning in smart education: From student modeling to personalized recommendations. arXiv preprint arXiv:2402.01666.

Web 2.0 technologies support personalisation of the learning process. These tools enable users to create, share, and collaborate on digital content. For example, they allow students to build learning experiences that reflect their individual needs and interests. This includes blogs, e-portfolios, wikis, social networks, forums, podcasts, collaborative productivity tools, and content-sharing platforms.

Resources

• top-10 personalized learning platforms
• Using Web 2.0 tools to engage learners
• Web 2.0 in the Schools
• Personal learning Environments based on Web 2.0 services in higher education

References

• Hamdan, A., Din, R., Manaf, S. Z. A., Salleh, N. S. M., Kamsin, I. F., Ab Khalid, R., ... & Karim, A. A. (2015). Personalized learning environment: Integration of Web 2.0 technology in achieving meaningful learning. Journal of Personalized Learning, 1(1), 13–26.
• Howe, E. L., & Kekwaletswe, R. M. (2012). Personalized learning support through Web 2.0: A South African context. Journal of Educational Technology, 8(4), 42–51.
• Kompen, R. T., Edirisingha, P., Canaleta, X., Alsina, M., & Monguet, J. M. (2019). Personal learning environments based on Web 2.0 services in higher education. Telematics and Informatics, 38, 194–206.
• McLoughlin, C., & Lee, M. J. (2008). Future learning landscapes: Transforming pedagogy through social software. Innovate: Journal of Online Education, 4(5).
• McLoughlin, C., & Lee, M. J. (2010). Personalised and self-regulated learning in the Web 2.0 era: International exemplars of innovative pedagogy using social software. Australasian Journal of Educational Technology, 26(1).

Semantic Web technologies are technologies that enable data and web content to be structured, connected, and interpreted meaningfully by computers. Instead of simply displaying information for humans to read, the Semantic Web allows systems to understand relationships between data, concepts, and learning resources, while enhancing reasoning and prediction capabilities through techniques such as knowledge graph embeddings. The Semantic Web extends traditional web technologies by adding semantic descriptions (“semantics” meaning) to information through metadata, ontologies, and linked relationships, allowing computers to understand, combine, and analyse data more effectively. Personalisation can be achieved by enabling intelligent systems and technologies, such as Natural Language Processing (NLP), to organise, retrieve, recommend, and adapt content automatically.

Common Semantic Web technologies include:

• RDF (Resource Description Framework), RDF - schema: used to describe relationships between data and resources.
• OWL (Web Ontology Language): used to create ontologies or knowledge structures that define concepts and relationships.
• SPARQL: a query language used to retrieve and manipulate semantic data.
• Metadata standards: used to describe learning resources using attributes such as topic, difficulty, format, or learning objectives.

References

• Agarwal, A., Mishra, D. S., & Kolekar, S. V. (2022). Knowledge-based recommendation system using semantic web rules based on learning styles for MOOCs. Cogent Engineering, 9(1), 2022568.
• Aktas, C., & Ciloglugil, B. (2023). A survey of semantic web based recommender systems for e-learning. In International Conference on Computational Science and Its Applications (pp. 494–506). Springer Nature Switzerland.
• Auer, S. (2022). Semantic integration and interoperability. In Designing data spaces: The ecosystem approach to competitive advantage (pp. 195–210). Springer International Publishing.
• Ferreira, H. N. M., Brant-Ribeiro, T., Araújo, R. D., Dorça, F. A., & Cattelan, R. G. (2016). An automatic and dynamic student modeling approach for adaptive and intelligent educational systems using ontologies and Bayesian networks. In 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) (pp. 738–745). IEEE.
• Halimi, K., Seridi-Bouchelaghem, H., & Faron-Zucker, C. (2014). An enhanced personal learning environment using social semantic web technologies. Interactive Learning Environments, 22(2), 165–187.
• Jensen, J. (2019). A systematic literature review of the use of Semantic Web technologies in formal education. British Journal of Educational Technology, 50(2), 505–517.
• Jevsikova, T., Berniukevičius, A., & Kurilovas, E. (2017). Application of resource description framework to personalise learning: Systematic review and methodology. Informatics in Education, 16(1), 61–82.
• Joy, J., Raj, N. S., & G, R. V. (2019). An ontology model for content recommendation in personalized learning environment. In Proceedings of the Second International Conference on Data Science, E-Learning and Information Systems (pp. 1–6).
• Lin, L., & Wang, F. (2023). Adaptive learning system based on knowledge graph. In Proceedings of the 9th International Conference on Education and Training Technologies (pp. 1–7).
• Nancharaiah, B., Prasad, R. V. V. S. V., Bolla, J. V., Anakal, S., & Viji, D. (2024). Semantic web and AI: Knowledge representation and reasoning. Journal of Computational Analysis & Applications, 33(4).