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.

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.

Teachers’ spatial behaviours in the classroom can strongly influence students’ engagement, motivation and other behaviours that shape their learning. However, classroom teaching behav-iour is ephemeral, and has largely remained opaque to computational analysis. This paper presents a library called ‘Moodoo’ that can serve to automatically model how teachers make use of the classroom space by analysing indoor positioning traces. The system automatically ex-tracts spatial metrics (e.g. teacher-student ratios, frequency of visits to students’ personal spaces, presence in classroom spaces of interest, index of dispersion and entropy), mapping from the teachers’ low-level positioning data to higher-order spatial constructs.

In the context of learning systems, identifying causal relationships among information presented to the user, their behavior and cognitive effort required/exerted to understand and perform a task is key to building effective learning experiences, and to maintain engagement in learning processes. An unexplored question is whether our interaction with presented information affects our cognitive effort (and behaviour), or vice-versa. We investigate causal relationship between information presented and cognitive effort (and behaviour) in the context of two separate studies (N = 40, N = 98), and study the effect of instruction (active/passive task). We utilize screen-recordings and eye-tracking data to investigate the relationship among these variables. To investigate the causal relationships among the different measurements, we use Granger’s causality. Further, we propose a new method to combine two time-series from multiple participants for detecting causal relationships. Our results indicate that information presentation drives user focus size (behaviour), and that cognitive load (a measure of cognitive effort exerted) drives information presentation. This relationship is also moderated by instruction type and performance-level (high/low). We draw implications for design of educational material and learning technologies.

Feedback is a crucial element in helping students identify gaps and assess their learning progress. In online courses, feedback becomes even more critical as it is one of the resources where the teacher interacts directly with the student. However, with the growing number of students enrolled in online learning, it becomes a challenge for instructors to provide good quality feedback that helps the student self-regulate. In this context, this paper proposed a content analysis of feedback text provided by instructors based on different indicators of good feedback. A random forest classifier was trained and evaluated at different feedback levels. The results achieved outcomes up to 87% and 0.39 of accuracy and Cohen's κ, respectively. The paper also provides insights into the most influential textual features of feedback that predict feedback quality.

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.

Assessment in technology-rich environments has on one hand attracted the interest of many researchers, but on the other hand still remains an intriguing and open research issue. This paper is a systematic review of systematic reviews of studies that are focusing on the topic. The method followed included a search strategy, inclusion and exclusion criteria, and quality assessment indicators. A dedicated tool was used for the establishment of the latter. Eleven systematic reviews were included in the study. The focus of the study is in five innovative areas of interest, namely (1) adaptive or personalized learning, (2) data-informed or evidence-based approaches, (3) blending formal and informal learning, (4) cultivation of 21st century skills, and (5) game-based learning. The research questions revolve around the types of assessment used, the research methodologies or strategies, the social aspects/ planes, and future research directions.

The presented study tries to tackle an intriguing question of how user-generated data from current technologies can be used to reinforce learners' reflections, improve teaching practices, and close the learning analytics loop. In particular, the aim of the study is to utilize users' gaze to examine the role of a mirroring tool (i.e. Exercise View in Eclipse) in orchestrating basic behavioral regulation of participants engaged in a debugging task. The results demonstrated that students who processed the information presented in the Exercise View and acted upon it, improved their performance and achieved higher level of success than those who failed to do it. The findings shed a light how to capture what constitute relevant data within a particular context using gaze patterns, that could guide collection of essential learner-centred analytics for the purpose of designing usable and modular learning environments based on data-driven approaches.