The process of associating elements of learning content with concepts or skills that this content helps students to master is one of the critical steps in developing personalized educational systems. When these associations are properly established, the system can infer the growth of student understanding of separate knowledge components from the logs of their interactions with associated learning content and use it to adapt the learning process accordingly by targeting gaps in individual students’ knowledge. Unfortunately, crafting these links between learning content and knowledge components is a very time- and expertise-demanding process that has traditionally been performed manually by domain experts with the help of knowledge engineers. Recently, the power of Large Language Models has motivated a new generation of research on concept extraction from textual learning content. The work presented in this paper contributes to this trend while introducing two important innovations. First, our concept extraction process is guided by a human-authored ontology of the target domain - Python programming. Second, alongside a traditional expert evaluation of the concept extraction quality, we apply two additional validation approaches: one based on using an educational data mining technique (learning curves) and another utilizing the pedagogical expertise of teaching the target domain (learning content placement).

In this paper, we present an approach that enables a broad re-use of English-authored smart learning content by translating it into other languages. To make it possible, we integrated translation functionalities directly into a smart content authoring system and engaging human-AI collaboration. The approach has been used to translate a large volume of worked examples and completion problems in Python from English to Spanish. The translated content has been piloted in several universities in a Spanish-speaking country.

The paper focuses on a new type of interactive learning content for SQL programming - worked examples of SQL code. While worked examples are popular in learning programming, their application for learning SQL is limited. Using a novel tool for presenting interactive worked examples, Database Query Analyzer (DBQA), we performed a large-scale randomized controlled study assessing the value of worked examples as a new type of practice content in a database course. We report the results of the classroom study examining the usage and the impact of DBQA. Among other aspects, we explored the effect of textual step explanations provided by DBQA.

In this paper, we analyze the benefits of adopting user interfaces that offer control and transparency for searching in contexts of learning activities. Concretely, we conducted a user study with pharmacy students performing a problem-solving task in the course of a university lecture. The task involved finding scientific papers containing relevant information to solve a clinical case. Students were split into two independent groups and assigned one search tool to perform the task. The baseline group worked with PubMed, a popular search engine in the life sciences domain, whereas the second half of the class was assigned an exploratory search system (ESS) designed for control and transparency. In the analysis, we cover the objective and subjective dimensions of the task outcomes. Firstly, the objective analysis addresses the inherent difficulty of the search task in a learning scenario and identifies certain improvements in performance for those students using the ESS, most notably when searching for primary-source content. The subjective analysis investigates the human factors side, providing evidence that the ESS effectively increases the perception of control and transparency and is able to produce a better user experience. Lastly, we report on perceived learning as a subjective dimension measured separately from user experience.

Educational Recommender Systems (EdRecSys) are different in nature from conventional Recommender Systems (RecSys) --mostly related to e-commerce-- as the main goal of EdRecSys is supporting students learning' instead of maximizing users' satisfaction from consuming the recommended items. Thus, research on transparency for traditional RecSys is hard to transfer from e-commerce contexts to educational scenarios, as the level of knowledge of the end-user (i.e. the student) is crucial for generating and evaluating the impact of the recommendations on students' learning. In this paper I present the main idea of my thesis proposal, which aims to fill this gap by taking a user-centered approach that combines design and evaluation of personalized recommender algorithms and explanatory interfaces with students in real learning contexts.

The increasing utilization of massive open online courses has significantly expanded global access to formal education. Despite the technology's promising future, student interaction on MOOCs is still a relatively under-explored and poorly understood topic. This work proposes a multi-level pattern discovery through hierarchical discriminative tensor factorization. We formulate the problem as a hierarchical discriminant subspace learning problem, where the goal is to discover the shared and discriminative patterns with a hierarchical structure. The discovered patterns enable a more effective exploration of the contrasting behaviors of two performance groups. We conduct extensive experiments on several real-world MOOC datasets to demonstrate the effectiveness of our proposed approach. Our study advances the current predictive modeling in MOOCs by providing more interpretable behavioral patterns and linking their relationships with the performance outcome.

Guiding students to the learning activities that are most appropriate for their current level of knowledge is one of the goals that adaptive educational systems tried to achieve during the last decades. Recently, several attempts have been made to use Open Learner Models (OLM) as a tool for achieving this goal. While the original goal of OLM is to help students reflect about their own learning process, extending OLM with navigation support functionality enables students to take immediate actions towards improving their knowledge. In this work, we attempted to extend the navigation support functionality of OLM by developing a fine-grained OLM that offers student knowledge visualization on both topic and concept levels. The fine-grained OLM enables students to directly explore connections between their knowledge and available learning activities, making an informed decision about their next learning steps. To assess the impact of the new type of OLM, we evaluated several versions of it in a classroom study, while also comparing it with data from our earlier studies that featured a coarse-grained OLM. Our results suggest that the fine-grained OLM considerably impacts student choice of learning activities, making student learning more efficient. We also found that the specific design features of fine-grained OLM could affect students' confidence and persistence while selecting and attempting the learning activities.

Open Learner Models (OLM) show the learner model to users to assist their self-regulated learning by, for example, helping prompt reflection, facilitating planning and supporting navigation. OLMs can show different levels of detail of the underlying learner model, and can also structure the information differently. As a result, a trade-off may exist between the potential for better support for learning and the complexity of the information shown. This paper investigates students' perceptions about whether offering more and richer information in an OLM will result in more effective support for their self-regulated learning. In a first study, questionnaire responses relating to designs for six visualisations of varying complexity led to the implementation of three variations on one of the designs. A second controlled study involved students interacting with these variations. The study revealed that the most useful variation for searching for suitable learning material was a visualisation combining a basic coloured grid, an extended bar chart-like visualisation indicating related concepts, and a learning gauge.

Complex skill mastery requires not only acquiring individual basic component skills, but also practicing integrating such basic skills. However, traditional approaches to knowledge modeling, such as Bayesian knowledge tracing, only trace knowledge of each decomposed basic component skill. This risks early assertion of mastery or ineffective remediation failing to address skill integration. We introduce a novel integration-level approach to model learners' knowledge and provide fine-grained diagnosis: a Bayesian network based on a new kind of knowledge graph with progressive integration skills. We assess the value of such a model from multifaceted aspects: performance prediction, parameter plausibility, expected instructional effectiveness, and real-world recommendation helpfulness. Our experiments based on a Java programming tutor show that proposed model significantly improves two popular multiple-skill knowledge tracing models on all these four aspects.