During the last decade, the embracement of learner engagement in developing educational technologies has contributed to the amalgamation of favorable pedagogical practices and advanced learning tools. New opportunities for tailoring data-driven learning designs created optimal conditions for crafting personalized, interactive e-learning environments that foster successful learning outcomes. Although a plethora of metrics exist to capture engagement, there is a need for comprehensive research that incorporates both the learner’s subjective perceptions of their engagement and the objective indicators of their actual engagement. The goal of this research is twofold: first, we aim to investigate the relationships between the interaction data on student behavior in an e-learning environment and their self-reported engagement data, and second, to design a model for predicting students’ level of engagement based on the study findings. Statistical analysis was conducted using data from (n = 45) undergraduate students at the University of South-Eastern Norway who completed a one-semester programming course, to explore relationships between their engagement and behavior in the programming tutoring system. Artificial neural networks were then used to develop a prediction model for classifying students’ engagement levels, leveraging the algorithms’ adaptability to diverse input data structures and classification efficiency. The findings highlight the importance of e-learning features like coding exercises, topic-based assessments, and explanatory hints in fostering student engagement. They also demonstrate the feasibility of predicting engagement using learner activity, interaction time, and learning outcomes. The study provides insights that inform the development of future educational designs for personalized engagement detection and improved learning outcomes.

Over the past three decades, Trust in Automation (TiA) has been the subject of extensive research. However, a large portion of the research takes a “static” approach to modeling trust and views trust as a linear unidirectional phenomenon. This view fails to recognize that trust is a dynamic construct that changes over time as an outcome of prolonged interaction with automation. The present study aims to address this gap and explore the nonlinear dynamic nature of trust by developing a simulation model of Trust in Automation (TiA) that can demonstrate trust evolution, deterioration, and recovery within the context of supervisory control of Maritime Autonomous Surface Ships (MASS). Employing System Dynamics (SD) approach, the model captures trust’s non-linear and reciprocal nature through dynamic feedback loops, producing behavioral patterns consistent with empirical observations of trust. The simulation results showcase the crucial role of initial trust conditions and the alignment of expectations with system performance in fostering trust and effective automation use. The study also explores the timing of system malfunctions, revealing that early faults have a greater negative impact on trust compared to later faults of the same magnitude. We tested a segment of the proposed model in an experimental study involving 30 human participants to investigate the effects of automation malfunctions on operators’ trust and behavioral responses during the supervisory control of MASS. Results not only validated the proposed model but demonstrated a significant decline in perceived reliability and trust in automation as well as the monitoring strategy after the automation malfunction.

Educational games have become an efficient and engaging way to enhance learning. Analytics have played a critical role in designing contemporary educational games, with most game design elements leveraging analytics produced during gameplay and learning. The presented study tackles the complex construct of engagement, which has been the central piece behind the success of educational games, by investigating the role of analytics-driven game elements on players’ engagement. To do so, we implemented a casual word game incorporating game design elements relevant to learning and conducted a within-subjects study where 39 participants played the game for two weeks. We found that the frequency of use of different game elements contributed to different dimensions of engagement. Our findings show that five of the eight game elements implemented in the word game engage players on an emotional, motivational, and cognitive level, thus emphasizing the importance of engagement as a multidimensional construct in designing educational casual games that offer highly engaging experiences.

Online learning systems should support students preparedness for professional practice by equipping them with the necessary skills while keeping them engaged and active. In that regard, the development of online learning systems that support students’ development and engagement with programming is a challenging process. Early career computer science professionals are required not only to understand and master numerous programming concepts but also to efficiently learn how to apply them in different contexts. A prerequisite for an effective and engaging learning process is the existence of adaptive and flexible learning environments that are beneficial for both students and teachers. Students can benefit from personalized content adapted to their individual goals, knowledge, and needs; while teachers can be relieved from the pressure to uniformly and promptly evaluate hundreds of student assignments. This study proposes and puts into practice a method for evaluating learning content difficulty and students’ knowledge proficiency utilizing a modified Elo-rating method. The proposed method effectively pairs learning content difficulty with students’ proficiency, and creates personalized recommendations based on the generated ratings. The method was implemented in a programming tutoring system and tested with interactive learning content for object oriented-programming. By collecting quantitative and qualitative data from students who used the system for one semester, the findings reveal that the proposed method can generate recommendations that are relevant to students and has the potential to assist teachers in grading students by providing a more holistic understanding of their progress over time.

Project-based learning has been introduced in many university courses as a dynamic classroom approach that motivates active exploration of real-world problems. It is also proven as one of the most effective ways for students to acquire practical skills and deeper knowledge. However, while learning with technologies in project-based blended environments, students are expected to know how to cope with real-world complex issues. Hence, students from two universities participated in an exploratory study with a focus in motivational and emotional challenges as part of collaborative learning. In particular, the study explored what regulation strategies students practiced as an answer to the challenges they encountered in customer-driven project-based learning activities. Nonetheless, the broad idea is to understand in what ways collaborative learning can be beneficial or debilitating for students' progress, and how technology can support or influence positive outcomes.

The success of technology enhanced learning can be increased by tailoring the content and the learning resources for every student; thus, optimizing the learning process. This study proposes a method for evaluating content difficulty and knowledge proficiency of users based on modified Elo-rating algorithm. The calculated ratings are used further in the teaching process as a recommendation of coding exercises that try to match the user's current knowledge. The proposed method was tested with a programming tutoring system in object-oriented programming course. The results showed positive findings regarding the effectiveness of the implemented Elo-rating algorithm in recommending coding exercises, as a proof-of-concept for developing adaptive and automatic assessment of programming assignments.

Learning analytics are used to track learners' progress and empower educators and learners to make well-informed data-driven decisions. However, due to the distributed nature of the learning process, analytics need to be combined to offer broader insights into learner's behavior and experiences. Consequently, this paper presents an architecture of a learning ecosystem, that integrates and utilizes cross-platform analytics. The proposed cross-platform architecture has been put into practice via a Java programming course. After a series of studies, a proof of concept was derived that shows how cross-platform analytics amplify the relevant analytics for the learning process. Such analytics could improve educators' and learners' understanding of their own actions and the environments in which learning occurs.

Personalization of the e-learning systems according to the learner’s needs and knowledge level presents the key element in a learning process. E-learning systems with personalized recommendations should adapt the learning experience according to the goals of the individual learner. Aiming to facilitate personalization of a learning content, various kinds of techniques can be applied. Collaborative and social tagging techniques could be useful for enhancing recommendation of learning resources. In this paper, we analyze the suitability of different techniques for applying tag-based recommendations in e-learning environments. The most appropriate model ranking, based on tensor factorization technique, has been modified to gain the most efficient recommendation results. We propose reducing tag space with clustering technique based on learning style model, in order to improve execution time and decrease memory requirements, while preserving the quality of the recommendations. Such reduced model for providing tag-based recommendations has been used and evaluated in a programming tutoring system.

Collaborative tagging systems have grown in popularity over the Web in the last years based on their simplicity to categorize and retrieve content using open-ended tags. Besides helping user to organize his/her personal collections, a tag also can be regarded as a user's or expert's personal opinion expression. Thus, the tagging information can be used to make recommendations. In this paper, an innovative architecture for a tag-based recommender system dedicated to the e-learning environments is introduced. This system could support learners by recommending tags and learning resources, online learning activities or optimal browsing pathways, based on their preferences, learning style, knowledge level and the browsing history of other learners with similar characteristics.