Purpose: The rapid advancement of technology in education is driving the digital transformation of schools and educational systems, creating an increasing demand for impactful EdTech solutions. While entrepreneurship education is widely recognized for its benefits, the effectiveness of such programs within the EdTech sector remains underexplored. Research has yet to comprehensively examine how entrepreneurship education tailored to EdTech influences entrepreneurial self-efficacy (ESE) – a key determinant of entrepreneurial motivation, decision-making and success. This study employs a mixed-methods approach to evaluate an EdTech-focused entrepreneurship education program and its impact on participants’ ESE. Quantitative results indicate a statistically significant increase in participants’ self-efficacy across multiple dimensions, with medium to large effect sizes. The qualitative findings further reveal key mechanisms contributing to ESE growth, emphasizing the role of individualized mentoring, constructive feedback and a sector-specific focus. Notably, mentoring emerged as the most influential factor, enabling personalized learning experiences and exposure to entrepreneurial role models. This research contributes to both EdTech entrepreneurship and the broader field of learning technology by offering empirical evidence on how tailored entrepreneurship education programs can foster ESE. The findings underscore the importance of (1) explicitly addressing the unique challenges of the EdTech market, such as long sales cycles, and (2) integrating structured mentoring and coaching strategies to build participants' confidence. These insights provide a practical framework for designing and evaluating sector-specific entrepreneurship education programs, ultimately supporting the development of sustainable EdTech startups. Design/methodology/approach: The study adopted a mixed-methods approach to address the research questions. It draws on qualitative and quantitative data collected from questionnaires and interviews with aspiring entrepreneurs (participants) as well as their trainers and mentors. Findings: With regard to RQ1 (What is the impact of the entrepreneurship program on participants’ ESE?), the program was successful in enhancing participants’ ESE. The quantitative findings demonstrated statistically significant gains in ESE, with medium to large effect sizes. This indicates that the program had a meaningful impact on participants’ confidence in their entrepreneurial abilities, in line with previous research showing that business development training can significantly enhance self-efficacy. With regard to RQ2 (In what ways does the program contribute to participants’ ESE in the EdTech sector?), the qualitative findings provided rich insights into how ESE was developed within the context of EdTech entrepreneurship. Eight key themes emerged, which were organized into three pillars: ways of interaction, ways of teaching and coaching and program design specifics. Research limitations/implications: Understanding how EdTech entrepreneurship education programs impact participants’ ESE provides valuable insights for program design and helps predict performance outcomes, behaviors and decision-making. This study investigated ESE in the context of an entrepreneurship education program in the EdTech sector. Findings indicated that the program positively influenced participants’ ESE, with mentoring emerging as the most impactful component, as it enabled individualized feedback and provided realistic voices from successful entrepreneurs. Practical implications: The study contributed new insights on fostering ESE, with direct implications for future program design, specifically emphasizing industry-specific focus: tailoring programs to the unique challenges and opportunities of specific markets, such as EdTech, and confidence-building strategies: structuring mentoring and coaching frameworks to explicitly enhance self-efficacy and mitigate fear of failure. Industry-specific focus can significantly enhance program effectiveness, while explicitly incorporating ESE-building strategies can bolster the confidence of aspiring entrepreneurs. Social implications: As entrepreneurial learning is increasingly explored globally, researchers and practitioners must collaborate to share best pedagogical practices and improve program quality, particularly in rapidly evolving fields like EdTech. In this work, EdTech entrepreneurship is not considered a business-oriented research or practice field, but rather a driver for research and innovation around technological advancements in learning and education. Originality/value: Despite the promising potential of EdTech entrepreneurship, research has yet to comprehensively examine the quality and effectiveness of EdTech-specific entrepreneurship education programs. While some literature discusses entrepreneurship education in general, studies dedicated to the EdTech sector remain scarce. This study seeks to bridge this gap by investigating an EdTech entrepreneurship education program with a specific focus on its impact on participants' ESE. Understanding how such programs influence ESE – a construct associated with behaviors, decision-making and business success – is critical for improving program design and ensuring long-term success in EdTech ventures.

Design pattern creation, especially in the e-learning domain, is a highly complex process that has not been sufficiently studied and formalized. In this paper, we propose a systematic pattern development cycle, whose most important aspects focus on reverse engineering of existing systems in order to elicit features that are cross-validated through the use of appropriate, authentic scenarios. However, an iterative pattern process is proposed that takes advantage of multiple data sources, thus emphasizing a holistic view of the teaching – learning processes. The proposed schema of pattern mining has been extensively validated for Asynchronous Network Supported Collaborative Learning (ANSCL) systems, as well as for other types of tools in a variety of scenarios, with promising results.

During the last few years, there has been a growing interest in students getting engaged in digital game-making activities so as to enhance their thinking skills. The findings of studies that have examined the impact of such initiatives are quite positive, especially concerning the promotion of 21st century skills; however, many students seem to face difficulties in getting a deeper understanding of the game development life cycle. Additionally, students often have difficulties in meaningfully reusing and applying the concepts from various subjects, mainly mathematics and physics, into their game-making tasks or in understanding advanced programming commands while creating their games. The present study presents an innovative game-making teaching approach that suggests a series of steps for the systematic design and rapid development of motion-based touchless games, i.e., games that are based on natural user interaction technologies, like the Microsoft Kinect camera. Findings from evaluation studies in two (2) secondary schools indicate that this approach can increase student motivation, strengthen their computational thinking, enhance their understanding of geometric principles and improve their social skills.

Networked learning is much more ambitious than previous approaches of ICT-support in education. It is therefore more difficult to evaluate the effectiveness and efficiency of the networked learning activities. Evaluation of learners' interactions in networked learning environments is a difficult, resource and expertise demanding task. Educators participating in online learning environments, have very little support by integrated tools to evaluate students' activities and identify learners' online browsing behavior and interactions. As a consequence, educators are in need for non-intrusive and automatic ways to get feedback from learners' progress in order to better follow their learning process and appraise the online course effectiveness. They also need specialized tools for authoring, delivering, gathering and analysing data for evaluating the learning effectiveness of networked learning courses. Thus, the aim of this paper is to propose a new set of services for the evaluator and lecturer so that he/she can easily evaluate the learners' progress and produce evaluation reports based on learners' behaviour within a Learning Management System. These services allow the evaluator to easily track down the learners' online behavior at specific milestones set up, gather feedback in an automatic way and present them in a comprehensive way. The innovation of the proposed set of services lies on the effort to adopt/adapt some of the web usage mining techniques combining them with the use of semantic description of networked learning tasks