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The aim of this study is to investigate achievement profiles in mathematics when integers are taught in a learning study in grade three (to children 8–9 years old) and to explore to what extent students with such profiles participate in inclusive teaching and learning practices.

Data from a previous learning study are re-analysed, supported by a framework that enables the investigation of inclusive practices. In the present study, inclusion and achievement are viewed as interrelated, meaning that student achievement must be incorporated in the definition of inclusion. The analysis is based on documentation of a video-recorded lesson and on identical tests conducted before and after the lesson.

The general framing of the learning study indicates an inclusive practice, while pre- and post-test achievement together with data from the lesson reveal a mixed picture concerning student achievement and inclusion. The analysis of the pre- and post-test results for the 16 students in the class indicates considerable diversity in student achievement, resulting in four achievement profiles. The main conclusion is that some students gained from participating in the learning study lesson while others did not. The extensive analysis of four students' participation, one for each profile, shows that differences in student achievement are related to the extent to which students participate in inclusive teaching and learning practices.

While previous research on learning studies has mainly considered average student achievement, this study focuses on individual variation in achievement and the reasons for it, a matter largely neglected in previous learning study research.

The purpose of this paper is to develop and investigate the idea that self-profiling and career control by temporary agency workers (TAWs) in low-skill jobs are positively related to informal learning and that this relationship is mediated by job challenge.

An online survey study was conducted among 722 TAWs in low-skill jobs in the Netherlands. Bootstrap mediation analyses were used to test the hypotheses.

Self-profiling and career control are positively related to informal learning of TAWs and these relationships are mediated by job challenge.

This is the first study to develop and empirically test the proposition that self-profiling and career control are important factors for enhancing employees’ learning experiences in low-skill jobs.

Hiring companies and temporary work agencies could stimulate and train TAWs’ self-profiling and career control competencies to enhance their job challenge and informal learning. Organizations should consider assigning challenging tasks to TAWs, which may be a good alternative for expensive formal training programs.

Many TAWs in low-skill jobs do not possess the skills and capacities to obtain a better or more secure job. In general, temporary workers face a higher risk of unemployment and greater income volatility (Segal and Sullivan, 1997). Gaining knowledge about how to develop this group is important for society as a whole.

Research on the determinants of informal learning mainly concerned higher-educated employees and managers with long-term contracts (e.g. Dong et al., 2014), whereas very little is known about factors that stimulate informal learning among TAWs in general, and among TAWs in low-skill jobs in particular.

This paper argues that all parties in care settings ‐ that is, older people and their carers, qualified and unqualified staff and students ‐ have learning needs, and that an appreciation of these needs is essential if high‐quality care and a positive work environment are to be achieved. It describes the rationale for, and the development of, the Profile of Learning Achievements in Care Environments (PLACE) approach. Building on the notions of relationship‐centred care and underpinned by the 'Senses Framework', PLACE seeks to provide a toolkit for identifying learning needs, establishing what seems to be working well and agreeing areas in need of improvement. The theory underpinning PLACE and the methodology for its development are described and potential areas for application are considered.

This paper aims to understand what drives people – their motivations, autonomous learning attitudes and learning interests – to volunteer as mentors for a program that helps families to ideate technological solutions to community problems.

A three-phase method was used to build volunteer mentor profiles; elicit topics of interest and establish relationships between those. The mentor profiles were based on self-assessments of motivation, attitude toward lifelong learning and self-regulated learning strategies. The topics of interest were elicited through content analysis of answers to reflection questions. Statistical methods were applied to analyze the relationship between the interests and the mentor profiles.

Bottom-up clustering led to the identification of three mentor groups (G1 “low”; G2 “high” and G3 “medium”) based on pre-survey data. While content analysis led to identifying topics of interest: communication skills; learning AI; mentoring; prototype development; problem-solving skills; working with families. Analyzing relationships between mentor profile and the topics of interest, the group G3 “medium,” with strong intrinsic motivation, showed significantly more interest in working with families. The group with the overall highest scores (G2 “high”) evidenced also substantial interest in learning about AI, but with high variability between members of the group.

The study established different types of learning interests of volunteer mentors and related them to the mentor profiles based on motivation, self-regulated learning strategies and attitudes toward lifelong learning. Such knowledge can help organizations shape the volunteering experience to provide more value to volunteers. Furthermore, the reflection questions can be used by volunteers as an instrument for reflection and by organizations to elicit the learning interests of volunteers.

Although the role of reflections in entrepreneurship education is undeniable, the research has focused mainly on their advantages and consequences for learning process, whereas their dynamics and interrelations with other mental processes remain unexplored. The purpose of this paper is to better understand how personality and intelligence constructs: cognition, conation, and affection evolve and change along the learning process during entrepreneurship education.

To better understand reflective processes in entrepreneurial learning this paper adopts the tripartite constructs of personality and intelligence. By employing longitudinal explorative research approach and self-organizing map (SOM) algorithm, the authors follow students’ reflections during their two-year learning processes. First, the authors try to identify how the interplay between the cognitive, conative, and affective aspects emerges in students’ reflections. Then, the authors investigate how this interplay evolves during the individual learning process and finally, by looking for similarities in these learning pathways, the authors aim to identify patterns of students’ reflective learning process.

All constructs are present during the learning process and all are prone to change. The individual constructs alone shed no light on the interplay between different constructs, but rather that the interplay between sub-constructs should be taken into consideration as well. This seems to be particularly true for cognition, as procedural and declarative knowledge have very different profiles. Procedural knowledge emerges together with emotions, motivation, and volition, whereas the profile of declarative knowledge is individual. The unique profile of declarative knowledge in students’ reflections is an important finding as declarative knowledge is regarded as the center of current pedagogic practices.

The study broadens the understanding of reflective practices in the entrepreneurial learning process and the interplay between affective, cognitive, and conative sub-constructs and reflective practices in entrepreneurship education. The findings clearly indicate the need for further research on the interplay between sub-constructs and students’ reflection profiles. The authors see the study as an attempt to apply an exploratory statistical method for the problem in question.

The results are able to advise pedagogy. Practical implications concern the need to develop reflective practises in entrepreneurial learning interventions to enhance all three meta-competencies, even though there are so far no irrefutable findings to indicate that some types of reflection may be better than others.

The results of the analysis indicate that it is possible to study the complex and dynamic interplay between sub-constructs of cognitive, conative and affective constructs. Moreover, the research succeeded in identifying both individual variations and general reflection patterns and changes in these during the learning process. This was possible by adopting a longitudinal explorative research approach with SOM analyses.

The purpose of this study is to explore the perception of the users concerning the role of artificial intelligence (AI) in enhancing personal learning profile (PLP), personal learning network (PLN) and personal learning environment (PLE) and their effect on the perceived ease of use, perceived effectiveness and perceived usefulness for enhancing the overall attitude and satisfaction of the e-learning.

The data were collected from students and professionals who have ever used the e-learning module, and smart partial least square-structural equational modeling (PLS-SEM) is used to see relations between the different variables.

It was seen that the PLE is affecting both perceived ease of use and perceived usefulness. The research has shown that perceived ease of use showed a mediating effect between PLE and attitude and satisfaction. Further satisfaction mediates between perceived ease of use and intention. PLP has come out to significantly impacting perceived effectiveness. The multigroup analysis also showed that the attitude and satisfaction level affecting intention to use the e-learning module differ across the two groups of learners, i.e. gender and type of learners.

The data are collected from students and professionals who have ever used the e-learning module and wholly based on their perceptions, leading to self-perception bias.

The current research is trying to integrate the user perception of PLP, PLN, PLE into the framework of the technology acceptance model and see how they impact the overall attitude and satisfaction of the learners. AI can be used to improve them and make e-learning more adherent to the users. AI can play an essential role in generating the right environment by matching the profile of the learner.

The purpose of this study is to propose a knowledge‐based mobile learning framework that integrates various types of museum‐wide content, and supports ubiquitous, context‐aware, personalized learning for museums.

A unified knowledge base with multi‐layer reusable content structures serves as the kernel component to integrate content from exhibitions for education and collection in a museum. The How‐Net approach is adopted to build a unified natural and cultural ontology. The ontology functions as a common and sharable knowledge concept that denotes each knowledge element in the unified knowledge base, and associates each learner's learning context and usage with a content and usage profile respectively. Data mining algorithms, e.g. association mining and clustering, are applied to discover useful patterns for ubiquitous personalization from these content and usage profiles.

A pilot project based on the proposed framework has been successfully implemented in the Life Science Hall of the National Museum of Natural Science (NMNS), Taiwan, demonstrating the feasibility of this framework.

This study proposes a mobile learning framework that can be replicated in many museums. This framework improves learners' learning experiences with rich related content, and with ubiquitous, proactive and adaptive services. Museums can also benefit from implementing this framework through outreach services for educational, promoting and usability needs from combining mobile and Internet communication technologies and learning services.

The paper seeks to provide personalized learning objects to adults' educators of special groups (AESG) in a technology‐enhanced learning environment.

The paper presents a definition of specific criteria by which personalization of learning objects is effected. An analysis under the scope of adult education and multicultural education is performed, resulting in the development of tools and a clear path leading to more efficient personalization of learning objects of AESG within an e‐learning environment.

Personalization of learning objects for AESG can be achieved to a much greater extent when the element of compatibility between educator and learner defines content, preceding and mapping with presentation factors.

As the research is focused on AESG, the key notion of compatibility may not be applicable to adult educators of the general public.

The paper offers a path through which learning management systems can provide improved personalization of learning objects addressed to AESG.

The introduction of compatibility between educator and learner as the key element of the educator's profile in order to provide personalized learning objects addressed to AESG opens up new territory. The paper is also useful to the developers of learning management systems addressed to any group with special attributes which strongly affect the learner's profile.

The purpose of this research is to propose an evaluation framework for analyzing learning objects usage, with the aim of extracting useful information for improving the quality of the metadata used to describe the learning objects, but also for personalization purposes, including user models and adaptive itineraries.

The paper presents experimental results from the log usage analysis during one academic semester of two different subjects, 350 students. The experiment examines raw server log data generated from the interactions of the students with the classroom learning objects, in order to find relevant information that can be used to improve the metadata used for describing both the learning objects and the learning process.

Preliminary studies have been carried out in order to obtain an initial picture of the interactions between learners and the virtual campus, including both services and resources usage. These studies try to establish relationships between user profiles and their information and navigational behavior in the virtual campus, with the aim of promoting personalization and improving the understanding of what learning in virtual environments means.

During the formal learning process, students use learning resources from the virtual classroom provided by the academic library, but they also seek information outside the virtual campus. All these usage data are not considered in the model proposed here. Further research is needed in order to obtain a complete view of the seeking information behavior of students for improving the users' profile and creating better personalized services.

This paper suggests how a selection of fields used in the LOM standard could be used for enriching the description of learning objects, automatically in some cases, from the learning objects usage performed by an academic community.

From libraries beginnings, they have been a “quiet storage place”. With the development of digital libraries, they become a meeting place where explicit and implicit recommendations about information sources can be shared among users. Social and learning process interactions, therefore, can be considered another knowledge source.

Learners’ dispositional attributes form the foundations for their learning behaviour and therefore academic outcomes. This study aims to explore the dispositional attributes of postgraduate learners in coursework programs, and to understand the relationships between dispositional attributes and academic achievement at this level.

This study profiled the dispositions towards learning of 880 Master’s students in Australia, reported in an online survey. Statistical analysis was used to explore the possibility of underlying dispositional dimensions and latent clusters of participants within the cohort.

The profile of the cohort overall was as expected for an elite academic group, yet there was substantial variation between individuals. Cluster analysis identified three groups of students with meaningfully different dispositional profiles. Exploratory factor analysis revealed two underlying dispositional dimensions, representing epistemic and agentic attributes. Epistemic attributes were most closely related to academic achievement.

It is argued that students at Master’s level typically possess the agentic attributes necessary for effective self-regulation. At this level, therefore, epistemic attributes are more relevant for differentiating between higher and lower achieving students. The attainment of sophisticated epistemic attributes is in line with the stated goals of postgraduate education. This supports the explicit teaching of metacognitive and epistemic skills within postgraduate degrees.

This study contributes a detailed analysis of Master’s students’ dispositional profiles. Two underlying dispositional dimensions are identified, representing agentic and epistemic attributes. The importance of epistemic attributes for postgraduate academic achievement identifies an opportunity for targeted interventions to raise the quality of learning at this level.