As the online learning landscape evolves, the need for personalization is increasingly evident. Although educational resources are burgeoning, educators face challenges selecting materials that both align with intended learning outcomes and address diverse learner needs. Large Language Models (LLMs) are attracting growing interest for their potential to create learning resources that better support personalization, but verifying coverage of intended outcomes still requires human alignment review, which is costly and limits scalability. We propose a framework that supports the cost-effective automation of evaluating alignment between educational resources and intended learning outcomes. Using human-generated materials, we benchmarked LLM-based text-embedding models and found that the most accurate model (Voyage) achieved 79% accuracy in detecting alignment. We then applied the optimal model to LLM-generated resources and, via expert evaluation, confirmed that it reliably assessed correspondence to intended outcomes (83% accuracy). Finally, in a three-group experiment with 360 learners, higher alignment scores were positively related to greater learning performance, χ2(2, N = 360) = 15.39, p <.001. These findings show that embedding-based alignment scores can facilitate scalable personalization by confirming alignment with learning outcomes, which allows teachers to focus on tailoring content to diverse learner needs.

Modelling learning objects (LO) within their context enables the learner to advance from a basic, remembering-level, learning objective to a higher-order one, i.e., a level with an application- and analysis objective. While hierarchical data models are commonly used in digital learning platforms, using graph-based models enables representing the context of LOs in those platforms. This leads to a foundation for personalized recommendations of learning paths. In this paper, the transformation of hierarchical data models into knowledge graph (KG) models of LOs using text mining is introduced and evaluated. We utilize custom text mining pipelines to mine semantic relations between elements of an expert-curated hierarchical model. We evaluate the KG structure and relation extraction using graph quality-control metrics and the comparison of algorithmic semantic-similarities to expert-defined ones. The results show that the relations in the KG are semantically comparable to those defined by domain experts, and that the proposed KG improves representing and linking the contexts of LOs through increasing graph communities and betweenness centrality.

Informal learning procedures have been changing extremely fast over the recent decades not only due to the advent of online learning, but also due to changes in what humans need to learn to meet their various life and career goals. Consequently, online, educational platforms are expected to provide personalized, up-to-date curricula to assist learners. Therefore, in this paper, we propose an Artificial Intelligence (AI) and Crowdsourcing based approach to create and update curricula for individual learners. We show the design of this curriculum development system prototype, in which contributors receive AI-based recommendations to be able to define and update high-level learning goals, skills, and learning topics together with associated learning content. This curriculum development system was also integrated into our personalized online learning platform. To evaluate our prototype we compared experts’ opinion with our system’s recommendations, and resulted in 89%, 79%, and 93% F1-scores when recommending skills, learning topics, and educational materials respectively. Also, we interviewed eight senior level experts from educational institutions and career consulting organizations. Interviewees agreed that our curriculum development method has high potential to support authoring activities in dynamic, personalized learning environments.

Attaining those skills that match labor market demand is getting increasingly complicated, not in the last place in engineering education, as prerequisite knowledge, skills, and abilities are evolving dynamically through an uncontrollable and seemingly unpredictable process. Anticipating and addressing such dynamism is a fundamental challenge to twenty-first century education. The burgeoning availability of data, not only on the demand side but also on the supply side (in the form of open educational resources) coupled with smart technologies, may provide a fertile ground for addressing this challenge. In this paper, we propose a novel, Artificial Intelligence (AI) driven approach to the development of an open, personalized, and labor market oriented learning recommender system, called eDoer. We discuss the complete system development cycle starting with a systematic user requirements gathering, and followed by system design, implementation, and validation. Our recommender prototype (1) derives the skill requirements for particular occupations through an analysis of online job vacancy announcements; (2) decomposes skills into learning topics; (3) collects a variety of open online educational resources that address those topics; (4) checks the quality of those resources and topic relevance with three intelligent prediction models; (5) helps learners to set their learning goals towards their desired job-related skills; (6) recommends personalized learning pathways and learning content based on individual learning goals; and (7) provides assessment services for learners to monitor their progress towards their desired learning objectives. Accordingly, we created a learning dashboard focusing on three Data Science related jobs and conducted an initial validation of eDoer through a randomized experiment. Controlling for the effects of prior knowledge as assessed by means of a pretest, the randomized experiment provided tentative support for the hypothesis that learners who engaged with personal recommendations provided by eDoer to acquire knowledge of basic statistics, attained higher scores on the posttest than those who did not. The hypothesis that learners who received personalized content in terms of format, length, level of detail, and content type, would achieve higher scores than those receiving non-personalized content was not supported.

Open Educational Resources (OERs) are openly licensed educational materials that are widely used for learning. Nowadays, many online learning repositories provide millions of OERs. Therefore, it is exceedingly difficult for learners to find the most appropriate OER among these resources. Subsequently, the precise OER metadata is critical for providing high-quality services such as search and recommendation. Moreover, metadata facilitates the process of automatic OER quality control as the continuously increasing number of OERs makes manual quality control extremely difficult. This work uses the metadata of 8,887 OERs to perform an exploratory data analysis on OER metadata. Accordingly, this work proposes metadata-based scoring and prediction models to anticipate the quality of OERs. Based on the results, our analysis demonstrated that OER metadata and OER content qualities are closely related, as we could detect high-quality OERs with an accuracy of 94.6%. Our model was also evaluated on 884 educational videos from Youtube to show its applicability on other educational repositories.

Abstract With the increased dependence on online learning platforms and educational resource repositories, a unified representation of digital learning resources becomes essential to support a dynamic and multi-source learning experience. We introduce the EduCOR ontology, an educational, career-oriented ontology that provides a foundation for representing online learning resources for personalised learning systems. The ontology is designed to enable learning material repositories to offer learning path recommendations, which correspond to the user’s learning goals and preferences, academic and psychological parameters, and labour-market skills. We present the multiple patterns that compose the EduCOR ontology, highlighting its cross-domain applicability and integrability with other ontologies. A demonstration of the proposed ontology on the real-life learning platform eDoer is discussed as a use case. We evaluate the EduCOR ontology using both gold standard and task-based approaches. The comparison of EduCOR to three gold schemata, and its application in two use-cases, shows its coverage and adaptability to multiple OER repositories, which allows generating user-centric and labour-market oriented recommendations. Resource: https://tibonto.github.io/educor/ .

Open Educational Resources are becoming a significant source of learning that are widely used for various educational purposes and levels. Learners have diverse backgrounds and needs, especially when it comes to learners with accessibility requirements. Persons with disabilities have significantly lower employment rates partly due to the lack of access to education and vocational rehabilitation and training. It is not surprising therefore, that providing high quality OERs that facilitate the self-development towards specific jobs and skills on the labor market in the light of special preferences of learners with disabilities is difficult. In this paper, we introduce a personalized OER recommeder system that considers skills, occupations, and accessibility properties of learners to retrieve the most adequate and high-quality OERs. This is done by: 1) describing the profile of learners with disabilities, 2) collecting and analysing more than 1,500 OERs, 3) filtering OERs based on their accessibility features and predicted quality, and 4) providing personalised OER recommendations for learners according to their accessibility needs. As a result, the OERs retrieved by our method proved to satisfy more accessibility checks than other OERs. Moreover, we evaluated our results with five experts in educating people with visual and cognitive impairments. The evaluation showed that our recommendations are potentially helpful for learners with accessibility needs.

This conceptual paper reviews the current status of goal setting in the area of technology enhanced learning and education. Besides a brief literature review, three current projects on goal setting are discussed. The paper shows that the main barriers for goal setting applications in education are not related to the technology, the available data or analytical methods, but rather the human factor. The most important bottlenecks are the lack of students goal setting skills and abilities, and the current curriculum design, which, especially in the observed higher education institutions, provides little support for goal setting interventions.

In this paper, we suggest a novel method to aid lifelong learners to access relevant OER based learning content to master skills demanded on the labour market. Our software prototype 1) applies Text Classification and Text Mining methods on vacancy announcements to decompose jobs into meaningful skills components, which lifelong learners should target; and 2) creates a hybrid OER Recommender System to suggest personalized learning content for learners to progress towards their skill targets. For the first evaluation of this prototype we focused on two job areas: Data Scientist, and Mechanical Engineer. We applied our skill extractor approach and provided OER recommendations for learners targeting these jobs. We conducted in-depth, semi-structured interviews with 12 subject matter experts to learn how our prototype performs in terms of its objectives, logic, and contribution to learning. More than 150 recommendations were generated, and 76.9% of these recommendations were treated as useful by the interviewees. Interviews revealed that a personalized OER recommender system, based on skills demanded by labour market, has the potential to improve the learning experience of lifelong learners.

This Work in Progress Research paper departs from the recent, turbulent changes in global societies, forcing many citizens to re-skill themselves to (re)gain employment. Learners therefore need to be equipped with skills to be autonomous and strategic about their own skill development. Subsequently, high-quality, on-line, personalized educational content and services are also essential to serve this high demand for learning content. Open Educational Resources (OERs) have high potential to contribute to the mitigation of these problems, as they are available in a wide range of learning and occupational contexts globally. However, their applicability has been limited, due to low metadata quality and complex quality control. These issues resulted in a lack of personalised OER functions, like recommendation and search. Therefore, we suggest a novel, personalised OER recommendation method to match skill development targets with open learning content. This is done by: 1) using an OER quality prediction model based on metadata, OER properties, and content; 2) supporting learners to set individual skill targets based on actual labour market information, and 3) building a personalized OER recommender to help learners to master their skill targets. Accordingly, we built a prototype focusing on Data Science related jobs, and evaluated this prototype with 23 data scientists in different expertise levels. Pilot participants used our prototype for at least 30 minutes and commented on each of the recommended OERs. As a result, more than 400 recommendations were generated and 80.9% of the recommendations were reported as useful.

In the recent decade, online learning environments have accumulated millions of Open Educational Resources (OERs). However, for learners, finding relevant and high quality OERs is a complicated and time-consuming activity. Furthermore, metadata play a key role in offering high quality services such as recommendation and search. Metadata can also be used for automatic OER quality control as, in the light of the continuously increasing number of OERs, manual quality control is getting more and more difficult. In this work, we collected the metadata of 8,887 OERs to perform an exploratory data analysis to observe the effect of quality control on metadata quality. Subsequently, we propose an OER metadata scoring model, and build a metadata-based prediction model to anticipate the quality of OERs. Based on our data and model, we were able to detect high-quality OERs with the F1 score of 94.6%.

In this paper, we suggest a novel method to help learners find relevant open educational videos to master skills demanded on the labour market. We have built a prototype, which 1) applies text classification and text mining methods on job vacancy announcements to match jobs and their required skills; 2) predicts the quality of videos; and 3) creates an open educational video recommender system to suggest personalized learning content to learners. For the first evaluation of this prototype we focused on the area of data science related jobs. Our prototype was evaluated by in-depth, semi-structured interviews. 15 subject matter experts provided feedback to assess how our recommender prototype performs in terms of its objectives, logic, and contribution to learning. More than 250 videos were recommended, and 82.8% of these recommendations were treated as useful by the interviewees. Moreover, interviews revealed that our personalized video recommender system, has the potential to improve the learning experience.

Despite improved digital access to scholarly knowledge in recent decades, scholarly communication remains exclusively document-based. In this form, scholarly knowledge is hard to process automatically. We present the first steps towards a knowledge graph based infrastructure that acquires scholarly knowledge in machine actionable form thus enabling new possibilities for scholarly knowledge curation, publication and processing. The primary contribution is to present, evaluate and discuss multi-modal scholarly knowledge acquisition, combining crowdsourced and automated techniques. We present the results of the first user evaluation of the infrastructure with the participants of a recent international conference. Results suggest that users were intrigued by the novelty of the proposed infrastructure and by the possibilities for innovative scholarly knowledge processing it could enable.

Issues related to Ethics and Privacy have become a major stumbling block in application of Learning Analytics technologies on a large scale. Recently, the learning analytics community at large has more actively addressed the EP4LA issues, and we are now starting to see learning analytics solutions that are designed not only as an afterthought, but also with these issues in mind. The 2nd EP4LA@LAK16 workshop will bring the discussion on ethics and privacy for learning analytics to a the next level, helping to build an agenda for organizational and technical design of LA solutions, addressing the different processes of a learning analytics workflow.

The large-scale production, collection, aggregation, and processing of information from various learning platforms and online environments have led to ethical and privacy concerns regarding potential harm to individuals and society. In the past, these types of concern have impacted on areas as diverse as computer science, legal studies and surveillance studies. Within a European consortium that brings together the EU project LACE, the SURF SIG Learning Analytics, the Apereo Foundation and the EATEL SIG dataTEL, we aim to understand the issues with greater clarity, and to find ways of overcoming the issues and research challenges related to ethical and privacy aspects of learning analytics practice. This interactive workshop aims to raise awareness of major ethics and privacy issues. It will also be used to develop practical solutions to advance the application of learning analytics technologies.

Since the emergence of learning analytics in North America, researchers and practitioners have worked to develop an international community. The organization of events such as SoLAR Flares and LASI Locals, as well as the move of LAK in 2013 from North America to Europe, has supported this aim. There are now thriving learning analytics groups in North American, Europe and Australia, with smaller pockets of activity emerging on other continents. Nevertheless, much of the work carried out outside these forums, or published in languages other than English, is still inaccessible to most people in the community. This panel, organized by Europe's Learning Analytics Community Exchange (LACE) project, brings together researchers from five European countries to examine the field from European perspectives. In doing so, it will identify the benefits and challenges associated with sharing and developing practice across national boundaries.