A Categorization of Workplace Learning Goals for Multi-Stakeholder Recommender Systems: A Systematic Review

Recommender System was widely used in commercial websites for the past few years. These systems track past activities of customers and recommend them the relevant items. The emergence of E-learning activities over a few decades develops a variety of E-learning content available for virtual learning environments (VLE). A large amount of learning objects is present in E-learning repositories. Dealing with problems of the diversity of data, Educational Recommender System (ERS) plays a vital role in the educational sector. Educational recommender systems track the learners' past activities, know the users' preferences, assist the educators and learners, provide relevant content to learners, and enhance their learning outcomes. A personalized recommender system will intensify the learners' interest in particular content and reduce the course dropout rate. The recommender system makes the decision-making process for choosing the appropriate content easy for learners. ERS uses various approaches and technologies for assisting the learners' and helps them to run their learning process smoothly. Collaborative filtering, Content-based, and knowledge-based are the basic techniques of recommender systems. The research shows that a combination of these approaches will give more effective and efficient results. This mixing of approaches refers to the hybridization techniques. Traditional approaches with deep learning networks will improve the recommendations and provide results with higher accuracy. This paper provides how E-learning support recommender systems produce recommendations using different techniques. The technical results of recommender techniques help to find the best approach for making a recommender system in the future.

PurposeThe purpose of this paper is to review the existing workplace learning measures used in empirical studies in human resource development (HRD).Design/methodology/approachBy reviewing 141 studies on workplace learning published in six journals in the field of HRD, we identified nine measures for workplace learning. Tynjälä’s (2013) 3-P model of workplace learning was adopted as the framework to analyze the features of each measure in terms of presage, process and product.FindingsWorkplace Climate Questionnaire, Learning Opportunities Questionnaire, Approaches to Work Questionnaire and Self-regulated Learning in the Workplace Questionnaire belong to the presage category. Small Business Workplace Learning Survey and Workplace Learning Activities are categorized as the process dimension. The Questionnaire on Informal Workplace Learning Outcomes is in the product dimension. Informal Workplace Learning Survey and Workplace Adaptation Questionnaire are across the three categories.Research limitations/implicationsThe authors identified the issues of existing workplace learning measures to emphasize the importance of reliable and valid measures for workplace learning and to gain the attention of researchers concerning these issues.Practical implicationsThe findings can provide organizations and practitioners with insights and ideas on how to prepare employees to engage in diverse learning activities, how to support their learning activities and how to combine their learning activities with the existing job structure and work system.Originality/valueThis study is the first to review workplace learning measures in the field of HRD. The authors review the dimensions, items and reliability of each measure, and summarize the features of nine measures in terms of presage, process and product of workplace learning.

The use of programming online judges (POJs) has risen dramatically in recent years, owing to the fact that the auto-evaluation of codes during practice motivates students to learn programming. Since POJs have greater number of programming problems in their repository, learners experience information overload. Recommender systems are a common solution to information overload. Current recommender systems used in e-learning platforms are inadequate for POJ since recommendations should consider learners' current context, like learning goals and current skill level (topic knowledge and difficulty level). To overcome the issue, we propose a context-aware practice problem recommender system based on learners' skill level navigation patterns. Our system initially performs skill level navigation pattern mining to discover frequent skill level navigations in the POJ and to find learners' learning goals. Collaborative filtering (CF) and content-based filtering approaches are employed to recommend problems in the current and next skill levels based on frequent skill level navigation patterns. The sequence similarity measure is used to find the top k neighbors based on the sequence of problems solved by the learners. The experiment results based on the real-world POJ dataset show that our approach considering the learners' current skill level and learning goals outperforms the other approaches in practice problem recommender systems.

Learning with personalized recommender systems: A psychological view

A well-known challenge in vocational education and training (VET) programmes is the lack of coherence between school-based and workplace learning. The Norwegian VET model consists of two years of upper secondary education followed by two years of apprenticeship at work. This small-scale study of vocational teacher’s vocational didactics aims to investigate boundary learning at the VET Programme for Sales and Service at the Norwegian upper secondary level. The research question explored is: How can vocational teachers improve boundary learning for students in work-based placements in the subject vocational in-depth study (YFF)? The methodological approach is qualitative interviews with vocational teachers and the systematic developmental work of vocational didactics by vocational teachers at one school. Our study focuses on boundary learning using the theoretical approach from Akkerman and Bakker (2011a). The findings show how vocational teachers develop and use boundary objects such as systematic reflection and subject based assignments to connect work-based learning from YFF to school-based learning. Further, a systematic development of vocational didactics is possible with a new proposed model of vocational didactics for boundary learning. The conclusion discusses how a developmental approach in the vocational teacher group combined with theore-tical concepts could enhance the students workplace and boundary learning.

Recommendation systems (RS) have been used in many scenarios, from entertainment to health. Inside the RS area, Educational Recommendation Systems (ERS) are becoming popular, been used for different types of recommendations such as recommending materials, exercises, and learning paths. As ERS works in a different scenario of classics RS, ERS requires specific evaluation metrics. However, the task of evaluating ERS is difficult once the educational field has its features to be analyzed. To help other researchers in this field, this work presents a systematic mapping on methods used for evaluating ERS. This study analyzed 91 papers of the last five years and provide an overview of the main methodologies, subject, metrics, and trends in the evaluation of ERS.

In recent years, Educational Recommender Systems (ERSs) have attracted great attention as a solution towards addressing the problem of information overload in e-learning environments and providing relevant recommendations to online learners. These systems play a key role in helping learners to find educational resources relevant and pertinent to their profiles and context. So, it is necessary to identify information that helps learner’s profile definition and in identifying requests and interests. In this context, we suggest to take advantage of the annotation activity used usually in the learning context for different purposes and which may reflect certain learner’s characteristics useful as input data for the recommendation process. Therefore, we propose an educational recommender system of web services based on learner’s annotative activity to assist him in his learning activity. This process of recommendation is founded on two preparatory phases: the phase of modelling learner’s personality profile through analysis of annotation digital traces in learning environment realized through a profile constructor module and the phase of discovery of web services which can meet the goals of annotations made by learner via the web service discovery module. The evaluation of the developed annotation based recommendation system through empirical studies realized on groups of learners based on the Student’s t-test showed significant results.

Purpose The purpose of this systematic review is to examine the antecedents of workplace learning, focusing on the individual and contextual factors that facilitate or inhibit engagement in learning activities integrated into daily work. The review aims to provide insights into how workplace learning occurs and how it can be supported in contemporary work environments. Design/methodology/approach This study uses a systematic review methodology, ensuring a comprehensive and transparent synthesis of research. The review focuses on identifying individual and contextual antecedents influencing workplace learning, based on studies published between 2012 and 2022. A total of 73 studies were included after a rigorous selection process, analysing factors that impact workplace learning in evolving work environments. Findings The review identifies key individual factors, such as motivation and self-efficacy, alongside contextual influences like learning climate and job demands, that significantly impact workplace learning. Emerging trends include the increasing role of digital tools and hybrid work models, which highlight the evolving nature of workplace learning in contemporary organisations. Originality/value This review reflects the evolving nature of work and learning, emphasising the need for a more immediate and embedded understanding of how learning occurs in today’s workplaces. It aligns with the growing emphasis on learning “in the flow of work” and underscores the importance of organisations fostering adaptability and innovation through integrated learning practices. By focusing specifically on workplace learning, this review offers valuable insights for organisations aiming to support continuous learning in dynamic environments.

In recent years, recommender systems have been widely used to support online learning in educational institutions. However, there are still some challenges experienced by learners and researchers hindering the full implementation and utilization of recommender systems in e-learning environments. In this paper, we review the main learner and researcher related challenges of e-learning recommender systems. This was achieved by carrying out a systematic literature review of relevant journal papers on e-learning recommender systems with a view to identifying and classifying the challenges as either learner or researcher challenges. The results of the survey reveal that successful implementation and utilization of e-learning recommender systems is hindered by some challenges categorized in this review as learner and researcher challenges. The paper also identifies some possible solutions from different studies for alleviating the challenges as well as the limitations. The implications of this study will be vital in assisting learners and educational institutions utilize recommender systems to support online teaching and learning.

The rapid evolution of the Internet has resulted in the availability of huge volumes of online learning resources on the web. However, many learners encounter difficulties in retrieval of suitable online learning resources due to information overload. Besides, different learners have different learning needs arising from their differences in learner’s context and sequential access pattern behavior. Traditional recommender systems such as content based and collaborative filtering (CF) use content features and ratings, respectively, to generate recommendations for learners. However, for accurate and personalized recommendation of learning resources, learner’s context and sequential access patterns should be incorporated into the recommender system. Traditional recommendation techniques do not incorporate the learner’s context and sequential access patterns in computing learner similarities and providing recommendations; hence, they are likely to generate inaccurate recommendations. Furthermore, traditional recommender systems provide unreliable recommendations in cases of high rating sparsity. In this paper, we propose a hybrid recommendation approach combining context awareness, sequential pattern mining (SPM) and CF algorithms for recommending learning resources to the learners. In our recommendation approach, context awareness is used to incorporate contextual information about the learner such as knowledge level and learning goals; SPM algorithm is used to mine the web logs and discover the learner’s sequential access patterns; and CF computes predictions and generates recommendations for the target learner based on contextualized data and learner’s sequential access patterns. Evaluation of our proposed hybrid recommendation approach indicated that it can outperform other recommendation methods in terms of quality and accuracy of recommendations.

Contemporary learning theories and their implementations associated with information and communication technologies increasingly integrate social constructivist approaches in order to assist and facilitate the construction of knowledge. Social constructivism also highlights the important role of culture, learning attitude and behavior in the cognitive process. Modern e-learning systems need to include these psychological aspects in addition to knowledge construction in order to connect with long-standing pedagogical issues such as the decrease and lack of motivation for education. Current Social Networking Services (SNS) provide a platform where peers can express their passion, emotion, and motivation toward learning. Therefore, this research utilizes this platform to recommend motivational contents from peers for learning motivation enhancement (i.e., learners’ perception of their goal and purpose for learning). The proposed system consists of an SNS platform for learners to (1) express and evaluate their own goals for learning, (2) observe motivational messages from peers recommended from an LDA-based (latent Dirichlet allocation) model, and (3) evaluate their perceptions on motivational and psychological attributes. The LDA-based model recommends messages expressing diverse purposes for a shared goal by maximizing the topic divergence of Twitter messages. Learners’ self-evaluations show the positive and significant impact of observing diverse learning purposes from peers on intrinsic motivational attributes such as goal specificity, attainability, and on the confidence to achieve the desired outcome.

This paper is a preliminary study of the state of the art on Recommender Systems (RS) in Higher Education. This work presents the initial phase towards the development of a prototype of RS of personalized learning path (PLP). The prototype can be seen as a choice for higher education professors' training in ICT, considering Mobile Learning and Microlearning as a working paradigm. The literature about RS focused on Technology Enhanced Learning (TEL) is varied and extensive. However, the presence of RS in the application of PLP is scarce. This review was conducted by exploring the core subject in specialized databases of education and computing. The revised corpus consisted of open access and high impact publications developed from 1990 to 2018 in the context of education. In the process of reviewing the literature, ten studies based on RS for TEL were identified, analyzed and compared. These studies reveal the possibility of applying various techniques and frequent tasks of RS in E-Commerce to the field of RS with educational purposes. It demonstrates that RS to access to Microlearning educational resources in mobile environments are limited, thus making this research appropriate and relevant.

Recommender systems are increasingly being used in university or online education. However, recommender systems still have not found major usage in K12 education. This may be because of the unique challenges that recommender systems face when used by a young and diverse population. However, recommender systems for K12 education could provide many benefits for students and teachers, such as the simplification of personalized learning. Some of the issues with K12 educational recommender systems may be solved by the use of deep learning and implicit feedback. As such, we investigated the use of deep recommendation algorithms and implicit feedback for K12 educational recommender systems. To do this, we compared metrics for highly cited traditional and deep recommendation algorithms trained on explicit and implicit data. We found that recommendation algorithms using deep learning as a group do not differ in performance compared to traditional recommendation algorithms. We also found that the use of implicit feedback led to higher performance than using explicit feedback. The best performing algorithm used both deep learning and implicit feedback. We conclude that deep learning can be a benefit for K12 recommender systems, particularly when the ordered sequence of items is carefully accounted for. We also conclude that researchers and developers must carefully consider which feedback contributes the most information for learning.

Despite the increasing attention that workplace learning has been receiving in recent years, high-quality programme design for these programmes remains a challenge and the translation into practice remains sometimes inadequate. In the present study we build on the expertise of experienced practitioners and experts to better understand in what way workplace learning can be embedded across different contexts in formal educational programmes so that they contribute to the development of the competencies aimed for. The purpose of the present chapter is to examine in detail how students’ workplace learning can be integrated in formal educational programmes. To meet this aim, a Delphi study was conducted in order to elucidate and synthesize the view of experts and experienced practitioners. In the first round, we interviewed 14 experts. Our analyses focused on clear similarities and notably unusual responses and led to the formulation of further and deeper questions, which we subsequently presented to five respondents during a second round of the Delphi study. The study resulted in the description of a model for integrating workplace learning in formal educational programmes and can be used to evaluate or design workplace learning settings in different educational levels.

Making it real: The benefits of workplace learning in upper-secondary vocational education and training courses