Introduction: the article considers a systemic nature of the modern educational process and the necessity to provide education not only on the basis of a direct teacher–student interaction, but also ...indirectly, i.e. through distance forms. However, there is a problem of the lack of certain rules and requirements for creating online courses as completed parts of a specific industry or subject that is being taught. Purpose: to analyze the effective tools available in theoretical research and practice for progressive development of the lifelong learning system in Russia in the conditions of digitalization of the society with regard to the possibilities of both full-time and correspondence education. The specifics of the formation and inclusion of online courses in the educational process of departmental universities are studied separately. Methods: comparative legal, statistical methods of description, and interpretation. Results: the authors present a model of the lifelong learning system. The article discusses development of online courses that are supposed to make a lifelong learning system in Russia more effective and accessible to all categories of citizens. Capacities of online courses are analyzed and the algorithm for designing online courses in relation to the system considered is developed. The authors highlight features of forming an online course system for departmental universities, programs and the training regime, which may differ significantly from civilian universities. Conclusion: the use of the proposed methodology will form a unified approach and requirements for forming online courses to implement them in the educational process.
Experience replay (ER) is a widely-adopted neuroscience-inspired method to perform lifelong learning. Nonetheless, existing ER-based approaches consider very coarse memory modules with simple memory ...and rehearsal mechanisms that cannot fully exploit the potential of memory replay. Evidence from neuroscience has provided fine-grained memory and rehearsal mechanisms, such as the dual-store memory system consisting of PFC-HC circuits. However, the computational abstraction of these processes is still very challenging. To address these problems, we introduce the Dual-Memory (Dual-MEM) model emulating the memorization, consolidation, and rehearsal process in the PFC-HC dual-store memory circuit. Dual-MEM maintains an incrementally updated short-term memory to benefit current-task learning. At the end of the current task, short-term memories will be consolidated into long-term ones for future rehearsal to alleviate forgetting. For the Dual-MEM optimization, we propose two learning policies that emulate different memory retrieval strategies: Direct Retrieval Learning and Mixup Retrieval Learning. Extensive evaluations on eight benchmarks demonstrate that Dual-MEM delivers compelling performance while maintaining high learning and memory utilization efficiencies under the challenging experience-once setting.
This systematic literature review aimed to provide updated information on lifelong learning in educational research by examining theoretical documents and empirical papers from 2000 to 2022. This ...review sought to identify concepts, theories, and research trends and methods linked to lifelong learning in educational research in different countries. Our review findings showed that theoretical papers, such as reports, policies, and concepts of lifelong learning, are generally much more extensive than empirical studies. Word cloud analysis revealed that the most prominent concepts were lifelong learning skills, lifelong learning competencies, and the three types of lifelong learning (formal, nonformal, and informal). Following the inductive analysis, this study investigated three common research trends: conceptual framework or policies of lifelong learning, lifelong learning abilities, and influencing factors of lifelong learning and/or lifelong learning abilities. Regarding methodology, this study identified only three studies that used mixed methods, which is insufficient in the field. In addition, heterogeneity was also observed between research instruments in lifelong learning. Different data analysis techniques can be applied in this field, including content analysis, descriptive analysis, and inferential analysis. Finally, the participants involved in the examined studies were students, primary and secondary school teachers, undergraduates, postgraduates, student teachers, European Union Lifelong Learning experts, young adults, teacher educators, administrators, and academic staff.
During the widespread development of open access online course materials in the last two decades, advances have been made in understanding the impact of instructional design on quantitative outcomes. ...Much less is known about the experiences of learners that affect their engagement with the course content. Through a case study employing text analysis of interview transcripts, we revealed the authentic voices of participants and gained a deeper understanding of motivations for and barriers to course engagements experienced by students participating in Massive Open Online Courses (MOOCs). We sought to understand why learners take the courses, specifically Introduction to Chemistry or Data Analysis and Statistical Inference, and to identify factors both inside and outside of the course setting that impacted engagement and learning. Thirty-six participants in the courses were interviewed, and these students varied in age, experience with the subject matter, and worldwide geographical location. Most of the interviewee statements were neutral in attitude; sentiment analysis of the interview transcripts revealed that 80 percent of the statements that were either extremely positive or negative were found to be positive rather than negative, and this is important because an overall positive climate is known to correlate with higher academic achievement in traditional education settings. When demographic data was added to the sentiment analysis, students who have already earned bachelor's degrees were found to be more positive about the courses than students with either more or less formal education, and this was a highly statistically significant result. In general, students from America were more critical than students from Africa and Asia, and the sentiments of female participants' comments were generally less positive than those of male participants. An examination of student statements related to motivations revealed that knowledge, work, convenience, and personal interest were the most frequently coded nodes (more generally referred to as “codes”). On the other hand, lack of time was the most prevalently coded barrier for students. Other barriers and challenges cited by the interviewed learners included previous bad classroom experiences with the subject matter, inadequate background, and lack of resources such as money, infrastructure, and internet access. These results are enriched by illustrative quotes from interview transcripts and compared and contrasted with previous findings reported in the literature, and thus this study enhances the field by providing the voices of the learners.
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•Sentiment analysis revealed that participants were generally positive in statements about the courses and MOOCs in general.•Bachelor's degree learners were the most positive: a highly statistically significant result.•Knowledge, work, convenience, and personal interest were the most prevalent motivations.•Most interviewees were ambivalent about the certificates.•Lack of time was most common barrier; others were previous bad experience &inadequate background.
Across 40 chapters, learners, learning and work are situated within educational, organisational, social, economic and political contexts. Taken together, these contributions paint a picture of ...evolving perspectives of how scholars from around the world view developments in both theory and practice, and map the shifts in learning and work over the past two decades.
The main objective of the current study was to investigate the relationships between perceptions of lifelong learning, lifelong learning competencies and learning strategies. The study was ...exploratory in nature and used three research tools: the Lifelong Learning Questionnaire, Lifelong Learning Competencies Scale, and Teachers’ Learning Strategies Questionnaire. It recruited 300 teacher trainers from education degree colleges in Myanmar, using a random sampling method. A descriptive and independent
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-test, ANOVA and Dunnett’s test were used to analyse the research questions. The findings indicated that teacher trainers’ perceptions of lifelong learning and lifelong learning competencies were positively correlated. Moreover, they used learning strategies to improve their teaching competencies. Specifically, their highest competency was in learning how to learn, while their lowest competency was in mathematics and science. None of the research variables differed according to gender, education level or teaching service. Statistically significant differences between perception of lifelong learning, lifelong learning competencies and learning strategies were found for the respective geographical regions (lower and upper Myanmar). Multilingual competence, digital competence, learning to learn competence, citizenship competence, entrepreneurship competence and cultural awareness competencies varied by region, but literacy, mathematics and science competencies did not. Significant differences were noted in perceptions of lifelong learning and learning strategies, but not lifelong learning competencies with respect to age. Literacy competence, digital competence and citizenship competence differed by age, but teaching tenure only had an influence on digital competence. Lastly, the study found a highly positive correlation between lifelong learning competencies and learning strategies.
Sensor-based human activity recognition (HAR), i.e., the ability to discover human daily activity patterns from wearable or embedded sensors, is a key enabler for many real-world applications in ...smart homes, personal healthcare, and urban planning. However, with an increasing number of applications being deployed, an important question arises: how can a HAR system autonomously learn new activities over a long period of time without being re-engineered from scratch? This problem is known as continual learning and has been particularly popular in the domain of computer vision, where several techniques to attack it have been developed. This paper aims to assess to what extent such continual learning techniques can be applied to the HAR domain. To this end, we propose a general framework to evaluate the performance of such techniques on various types of commonly used HAR datasets. Then, we present a comprehensive empirical analysis of their computational cost and of their effectiveness of tackling HAR specific challenges (i.e., sensor noise and labels’ scarcity). The presented results uncover useful insights on their applicability and suggest future research directions for HAR systems.
To cope with real-world dynamics, an intelligent system needs to incrementally acquire, update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as continual learning, ...provides a foundation for AI systems to develop themselves adaptively. In a general sense, continual learning is explicitly limited by catastrophic forgetting, where learning a new task usually results in a dramatic performance drop of the old tasks. Beyond this, increasingly numerous advances have emerged in recent years that largely extend the understanding and application of continual learning. The growing and widespread interest in this direction demonstrates its realistic significance as well as complexity. In this work, we present a comprehensive survey of continual learning, seeking to bridge the basic settings, theoretical foundations, representative methods, and practical applications. Based on existing theoretical and empirical results, we summarize the general objectives of continual learning as ensuring a proper stability-plasticity trade-off and an adequate intra/inter-task generalizability in the context of resource efficiency. Then we provide a state-of-the-art and elaborated taxonomy, extensively analyzing how representative strategies address continual learning, and how they are adapted to particular challenges in various applications. Through an in-depth discussion of promising directions, we believe that such a holistic perspective can greatly facilitate subsequent exploration in this field and beyond.
We present an analysis of instructional design quality of 76 randomly selected Massive Open Online Courses (MOOCs). The quality of MOOCs was determined from first principles of instruction, using a ...course survey instrument. Two types of MOOCs (xMOOCs and cMOOCs) were analysed and their instructional design quality was assessed and compared. We found that the majority of MOOCs scored poorly on most instructional design principles. However, most MOOCs scored highly on organisation and presentation of course material. The results indicate that although most MOOCs are well-packaged, their instructional design quality is low. We outline implications for practice and ideas for future research.
•Instructional design quality of 76 randomly selected MOOCs was assessed.•Quality was determined from first principles, using a Course Scan instrument.•The majority of MOOCs scored poorly on most instructional design principles.•Most MOOCs scored highly on organisation and presentation of course material.•Although most MOOCs are well-packaged, their instructional design quality is low.
Enhancing learning scenarios with social robots, as well as gamification elements, has been shown to positively influence motivation, engagement, or even both. However, they have not been combined in ...a learning environment. For this contribution, we created a learning environment for students in higher education and implemented additions (social robot and gamification) based on guidelines for gamification in learning scenarios, and research on pedagogical agents. Using a 2x2 design for systematic investigation of gamification elements and social robots, we tested the impact of our learning environment on motivation and engagement across four conditions: with a social robot, gamification elements, both or neither. We found no significant increase in engagement or motivation when adding gamification elements or the social robot. Quite contrary to our expectations, we found an interaction effect when combining both additions, showing lower engagement. Based on our results and former research, we discuss possible reasons for this finding and potential improvements for future research.
•Prototyping of a learning environment.•Addition of social robot and gamification elements in a plug-and-play functionality.•2x2 design for addition/absence of a social robot and gamification elements.•Investigation of effects on engagement and motivation in an interactive study.
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