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The purpose of this study is to set up an evaluation model to predict massive open online courses (MOOC) learning performance by analyzing MOOC learners’ online learning behaviors, and comparing three algorithms – multiple linear regression (MLR), multilayer perceptron (MLP) and classification and regression tree (CART).

Through literature review and analysis of data correlation in the original database, a framework of online learning behavior indicators containing 26 behaviors was constructed. The degree of correlation with the final learning performance was analyzed based on learners’ system interaction behavior, resource interaction behavior, social interaction behavior and independent learning behavior. A total of 12 behaviors highly correlated to learning performance were extracted as major indicators, and the MLR method, MLP method and CART method were used as typical algorithms to evaluate learners’ MOOC learning performance.

The behavioral indicator framework constructed in this study can effectively analyze learners’ learning, and the evaluation model constructed using the MLP method (89.91%) and CART method (90.29%) can better achieve the prediction of MOOC learners’ learning performance than using MLR method (83.64%).

This study explores the patterns and characteristics among different learning behaviors and constructs an effective prediction model for MOOC learners’ learning performance, which can help teachers understand learners’ learning status, locate learners with learning difficulties promptly and provide targeted instructional interventions at the right time to improve teaching quality.

The purpose of this paper is to make a correlation analysis between students’ online learning behavior features and course grade, and to attempt to build some effective prediction model based on limited data.

The prediction label in this paper is the course grade of students, and the eigenvalues available are student age, student gender, connection time, hits count and days of access. The machine learning model used in this paper is the classical three-layer feedforward neural networks, and the scaled conjugate gradient algorithm is adopted. Pearson correlation analysis method is used to find the relationships between course grade and the student eigenvalues.

Days of access has the highest correlation with course grade, followed by hits count, and connection time is less relevant to students’ course grade. Student age and gender have the lowest correlation with course grade. Binary classification models have much higher prediction accuracy than multi-class classification models. Data normalization and data discretization can effectively improve the prediction accuracy of machine learning models, such as ANN model in this paper.

This paper may help teachers to find some clue to identify students with learning difficulties in advance and give timely help through the online learning behavior data. It shows that acceptable prediction models based on machine learning can be built using a small and limited data set. However, introducing external data into machine learning models to improve its prediction accuracy is still a valuable and hard issue.

Teams, teamwork and team learning have been the subject of many research studies over the last decades. This article aims at investigating and confirming the Team Learning Beliefs and Behaviours (TLB&B) model within a very specific population, i.e. police and firemen teams. Within this context, the paper asks whether the team's beliefs about the interpersonal context and the occurrence of three team learning behaviours (construction, co‐construction and constructive conflict) play a role in building and maintaining mutually shared cognition in a collaborative learning environment leading to a higher effectiveness. Self‐efficacy was added to the original model. Furthermore, the effect of team meeting frequency on the TLB&B model was investigated.

All constructs were measured using the validated Team Learning Beliefs and Behaviours Questionnaire completed with the self‐efficacy scale. Data were collected from 126 teams (n_individuals=769) and analysed using stepwise multi‐level regression analyses and analyses of variance.

The results show that the examined model generally applies to the data. Furthermore, self‐efficacy was found to be a valuable addition to the model.

This article validates an existing team learning model in a new context, namely that of response teams. Furthermore, it adds self‐efficacy as a predictor for team learning behaviours and team effectiveness. A multilevel‐approach was used as a valuable alternative of aggregating individual perceptions to team constructs.

The purpose of this research is to determine key behaviours to be efficient in identifying and developing employees' talent. The article aims to address the relationship between learning agility and job crafting, the influence between them, and how this relationship is built to improve performance and adaptability. For this purpose, the research has analysed which behaviours obtain the highest scores in both scales (job crafting and learning agility), designing the tool which allows Human Resources (HR) professionals an efficient identification and development behaviours to get the versatile talent that companies and professionals of the future need.

Using the questionnaire that has integrated the learning agility scale and the Spanish job crafting scale. Data were collected from a sample of business professionals in Spain. Factor analysis and hierarchical cluster analysis were used, using a classificatory variable with the 126 valid responses obtained.

In an ever-changing environment, continuous employee adaptation to his/her role within a company is a critical factor for its survival. However, there is a paucity of large-scale empirical research on which behaviours employees have to develop to increase their adaptative skills. Drawing on the outcome of extant literature, the authors identify learning agility as the construct that firms have to encourage in their employees to impact job crafting. The contribution of the paper is twofold: (1) the authors empirically explored the association and the effects of learning agility and its factor on the development of job crafting. Results demonstrated the association between the two constructs; further, higher scores in both learning agility and job crafting predict increased employability, and higher scores in job crafting are associated with higher scores in change agility; (2) this study provides a multidimensional instrument that provides HR departments with the key behaviours to recruit in order to develop talent to prepare employees to face future challenges, ensuring the right performance and sustainable impact in the environment.

A limitation of this study is that it is done exclusively within Spanish companies, even though from different industries and with different characteristics. Therefore, future research is necessary and should be conducted in other countries in similar industries to explore the empirical findings from this study in additional contexts.

This research has found a tool that might allow HR departments to measure what level of job crafting and learning agility their employees have and to identify what key behaviours they need to focus on in the recruitment or in their internal strategic HR action plan to overcome any future challenges in their organization.

In a scenario where artificial intelligence is modifying the professional landscape, generating uncertainty about which skills are best to develop, the results are a guide for enterprises as to where to focus plans for learning and training, as well as for business schools regarding the content provided in training programs.

The authors advance the literature by providing a theoretical base for understanding the relationship between job crafting and learning agility. This article offers some practical managerial recommendations that help the human resources department focus on behaviours that allow talent to be identified and recruited to ensure an effective organization.

This study aims to explore, as an answer to the observed lack of knowledge about actual team learning behaviours, the characteristics of the actual observed basic team learning behaviours and facilitating team learning behaviours more in-depth of three project teams. Over time, team learning in an organisational context has been investigated more and more. In these studies, there is a dominant focus on team members’ perception of team learning behaviours.

A coding schema is created to observe actual team learning behaviours in interaction between team members in two steps: verbal contributions by individual team members are coded to identify the type of sharing behaviour and, when applicable, these individual verbal behaviours are build up to basic and facilitating team learning behaviours. Based on these observations, an analysis of team learning behaviours is conducted to identify the specific characteristics of these behaviours.

An important conclusion of this study is the lack of clarity about the line of demarcation between individual contributions and learning behaviours and team learning behaviours. Additionally, it is clear that the conceptualisations of team learning behaviour in previous research neglect to a large extend the nuances and depth of team learning behaviours.

Due to the innovative approach to study team learning behaviours, this study is of great value to the research field of teamwork for two reasons: the creation of a coding schema to analyse team learning behaviours and the findings that resulted from this approach.

To support achieving sustainable development goals (SDGs), we integrated science, technology, engineering and math (STEM) and extended reality technologies into an artificial intelligence (AI) learning activity. We developed Feature City to facilitate students' learning of AI concepts. This study aimed to explore students' learning outcomes and behaviors when using Feature City.

Junior high school students were the subjects who used Feature City in an AI learning activity. The learning activity consisted of 90-min sessions once per week for five weeks. Before the learning activity, the teacher clarified the learning objectives and administered a pretest. The teacher then instructed the students on the features, supervised learning and unsupervised learning units. After the learning activity, the teacher conducted a posttest. We analyzed the students' prior knowledge and learning performance by evaluating their pretest and posttest results and observing their learning behaviors in the AI learning activity.

(1) Students used Feature City to learn AI concepts to improve their learning outcomes. (2) Female students learned more effectively with Feature City than male students. (3) Male students were more likely than female students to complete the learning tasks in Feature City the first time they used it.

Within SDGs, this study used STEM and extended reality technologies to develop Feature City to engage students in learning about AI. The study examined how much Feature City improved students' learning outcomes and explored the differences in their learning outcomes and behaviors. The results showed that students' use of Feature City helped to improve their learning outcomes. Female students achieved better learning outcomes than their male counterparts. Male students initially exhibited a behavioral pattern of seeking clarification and error analysis when learning AI education, more so than their female counterparts. The findings can help teachers adjust AI education appropriately to match the tutorial content with students' AI learning needs.

Course recommendations are important for improving learner satisfaction and reducing dropout rates on massive open online course (MOOC) platforms. This study aims to propose an interpretable method of analyzing students' learning behaviors and recommending MOOCs by integrating multiple data sources.

The study proposes a deep learning method of recommending MOOCs to students based on a multi-attention mechanism comprising learning records attention, word-level review attention, sentence-level review attention and course description attention. The proposed model is validated using real-world data consisting of the learning records of 6,628 students for 1,789 courses and 65,155 reviews.

The main contribution of this study is its exploration of multiple unstructured information using the proposed multi-attention network model. It provides an interpretable strategy for analyzing students' learning behaviors and conducting personalized MOOC recommendations.

The findings suggest that MOOC platforms must fully utilize the information implied in course reviews to extract personalized learning preferences.

This study is the first attempt to recommend MOOCs by exploring students' preferences in course reviews. The proposed multi-attention mechanism improves the interpretability of MOOC recommendations.

This study was based on situated learning, by combining mobile learning and augmented reality, so that students could not only access information content in a real environment but also obtain such information via augmented reality, to support mobile learning.

The research included development of an augmented reality system combined with situational learning, used by students to learn about campus plants as part of the college life technology curriculum. Students took part in mobile learning, and an investigation was conducted into the computer learning behaviour of notebook users. College students were used as the experimental subjects. Data were collected using questionnaire surveys and were evaluated in order to identify the behavioural intentions of learners in outdoor learning activities.

The questionnaire survey covered environmental interactivity, system quality and textbook content. It was found that learners who used mobile learning augmented reality (MLAR) generally managed to browse all the contents of the textbook at each learning location, without spending too much time looking for information, and learners could quickly integrate this into the learning situation. Learners who used MLAR had a strong motivation to study plants at the learning site because they wanted to use the augmented reality technology to observe virtual plant models. Learners who used MLAR in their field learning liked using augmented reality for further learning, for example, using a magic wand to interact with the technology.

This study adopted a new approach to deliver elements of the life technology curriculum, integrating augmented reality into mobile learning. All participating students gave positive reviews of six aspects of the proposed system: their behavioural intentions, cognitive usefulness, cognitive ease of use, environmental interactivity, system quality and textbook content.

In conducting a multi-disciplinary, multi-degree study of all 83 higher education accrediting organizations in the United States and the 605 academic programs associated with them, our goal was to uncover patterns in the presence of leadership and general workforce competencies identified within the stated learning outcomes employed by these accrediting organizations. Our findings suggest strong variability across categories of leadership competence related to workforce competencies, where skills related to reasoning and communication were emphasized much more heavily than others such as intrapersonal development. These findings emerged across all postsecondary degree levels, from pre-baccalaureate to graduate programs, raising important questions for the leadership development of post-secondary students. Keywords: outcomes assessment, student leadership, professional development, leadership education, workforce development, competencies.

While colleges and universities often make the case that preparing students for future career success is critical, studies that examine the empirical support for the assertion curiously lag behind the advanced rhetoric. This paper will showcase research findings based on an analysis of 36,327 learning outcomes addressed within all 83 higher education accrediting organizations in the United States, representing 605 distinct postsecondary academic programs. Our goal was to uncover any patterns of emphasis in particular workforce and leadership competencies embedded within those learning outcomes and examine the extent to which those competencies are represented similarly across postsecondary degree levels.

Social learning theory specifically acknowledges that most human behaviour is learned observationally through modelling. The focus of this approach has been teaching leadership across formal and informal settings. This and the behavioural focus is what distinguishes social learning theory from others as a leadership theory. However it will not become a leadership theory unless the behaviours to be imparted to future leaders are outlined. This has not been done in the social learning context. However, because of its growing importance as a theoretical foundation for the fields of psychology and organisational behaviour as a whole, a social learning approach to leadership would seem to have potential for the future.