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This study aims to propose a blackboard approach using multistrategy machine learning student modeling techniques to learn the properties of students' inconsistent behaviors during their learning process.

These multistrategy machine learning student modeling techniques include inductive reasoning (similarity‐based learning), deductive reasoning (explanation‐based learning), and analogical reasoning (case‐based reasoning).

According to the properties of students' inconsistent behaviors, the ITS (intelligent tutoring system) may then adopt appropriate methods, such as intensifying teaching and practicing, to prevent their inconsistent behaviors from reoccurring.

This research sets the learning object on a single student. After the inferences are accumulated from a group of students, what kinds of students tend to have inconsistent behaviors or under what conditions the behaviors happened for most students can be learned.

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.

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.

It is common nowadays to hear people speak of Management by Objectives. But it is not so common to hear the relevant but often ignored question, ‘By whose objectives?’ If the question were asked someone designing an effective ‘management’ learning programme, the answer should be loud and clear as ‘by the learner's objectives.’ This is so because the objectives of the learner are in full control of what is learned, how fast it is learned, how well it is learned, and for how long it stays learned. That is, the learner makes the choice of what is learned and/or unlearned, he learns what impresses him most (whether he is expected to learn such things or not) and leaves out the experiences that do not impress him at all (whether they are ‘important’ or not). This assertion of conscious will on the behaviour of the adult human being makes it imperative that for any learning programme to be effective, the learning material to be presented to the adult, must be made to be as near as possible to what he would have freely chosen (to satisfy his learning needs — why want to learn) in terms of content (what to learn with), methodology(ies) (how to learn), time (when to learn), duration (for how long to learn formally, location (where to learn), and facilitators (people to help him learn). Hence the need for a designer to put all these items together optimally for effective learning.

The main purpose of this study is to develop an empirically grounded model of entrepreneurial learning that focuses on learning behaviours.

Based on the competency approach of understanding entrepreneurial learning, a qualitative study was conducted on a sample of 12 successful entrepreneurs. The data were collected using semi‐structured interviews based on the critical incident approach. A two‐stage approach was adopted in the data analysis.

Six patterns of entrepreneurial learning behaviours emerged from the analysis, and they were exerted in three transformative processes, namely accumulating experience through carrying out entrepreneurial tasks, consolidating learning outcomes from experience, and applying or transferring one's own and others' learning outcomes when carrying out the tasks. Also, the processes were under the influence of the learning contexts and the learning behaviours were reinforced throughout the process. Based on the analysis, a model of entrepreneurial learning centred upon the learning behaviours was constructed empirically.

Entrepreneurial learning can be seen as an open, generative, iterative and self‐reinforcing process. Further investigation can be conducted on the intensity of entrepreneurial learning, its effectiveness, and its relationships with entrepreneurs' tasks, experience, learning outcomes, and learning contexts.

Education and training for entrepreneurs should be situated within the actual workplace or simulated contexts that provide them with opportunities to apply what they have learned while taking action, to accumulate their first‐hand experience and to reflect upon experience.

Entrepreneurial learning is considered and examined as an observable and measurable construct with a focus on the learning behaviours exhibited by entrepreneurs.

The purpose of this paper is to highlight challenges and opportunities that surround the process of learning with an emphasis on higher-order learning and learning as behavior. Higher-order learning has been conceptualized as learning behavior that can be learned.

The holistic framework regarding higher-order learning has been proposed on the basis of systems perspective and critical thinking of previous contributions.

A review and analysis of learning, especially higher-order learning, resulted in its conceptualization and guidelines on how to implement it. Higher-order learning is a learning behavior that can be learned and implemented in many situations in complex social and organizational practices.

Conclusions and remarks provided in this paper need further empirical testing and validation.

Implications for practitioners have been identified in terms of recommendations for implementing higher-order learning as a learning behavior that can be learned.

Dedicated implementation of higher-order learning and learning as behavior can bring true change to the current social and economic paradigm and lasting solutions to the so-called “stubborn problems” of pollution, abuse, destruction and poverty, and can cause systemic transformation of our declining society.

Higher-order learning has been conceptualized and challenges surrounding it have been identified along with suggestions on how to overcome them.

This paper aims to introduce an imitation learning framework for a wheeled mobile manipulator based on dynamical movement primitives (DMPs). A novel mobile manipulator with the capability to learn from demonstration is introduced. Then, this study explains the whole process for a wheeled mobile manipulator to learn a demonstrated task and generalize to new situations. Two visual tracking controllers are designed for recording human demonstrations and monitoring robot operations. The study clarifies how human demonstrations can be learned and generalized to new situations by a wheel mobile manipulator.

The kinematic model of a mobile manipulator is analyzed. An RGB-D camera is applied to record the demonstration trajectories and observe robot operations. To avoid human demonstration behaviors going out of sight of the camera, a visual tracking controller is designed based on the kinematic model of the mobile manipulator. The demonstration trajectories are then represented by DMPs and learned by the mobile manipulator with corresponding models. Another tracking controller is designed based on the kinematic model of the mobile manipulator to monitor and modify the robot operations.

To verify the effectiveness of the imitation learning framework, several daily tasks are demonstrated and learned by the mobile manipulator. The results indicate that the presented approach shows good performance for a wheeled mobile manipulator to learn tasks through human demonstrations. The only thing a robot-user needs to do is to provide demonstrations, which highly facilitates the application of mobile manipulators.

The research fulfills the need for a wheeled mobile manipulator to learn tasks via demonstrations instead of manual planning. Similar approaches can be applied to mobile manipulators with different architecture.

The purpose of this paper is to test and explain the context where motivation to learn (MTL) reduces innovative behavior in the organizational context.

The authors used questionnaire survey to collect data in a field study. In order to test the moderating effect of transfer climate, MTL on the relationship between MTL and innovative behavior, a sample of 606 employees was analyzed to examine the theoretical expectation by using multiple regression and bootstrapping.

The authors found employees motivated to learn showed less innovative behavior when perceived transfer climate is less favorable. The authors further revealed that motivation to transfer mediates the moderating effect of transfer climate for the relationship between MTL and innovative behavior.

One suggestion for further research is to investigate the relationship among the four constructs by using multi-source, multi-wave and multi-level method.

This study provides several useful guidance of how organization and manager avoid the negative effects of MTL through encouraging employees to learn new knowledge and skills, and providing employee opportunities to use their acquired knowledge and skills.

The authors contribute to the motivational literature by taking a step further to understand the effect of MTL. The authors propose and confirm that employee MTL can lead to negative outcomes when individuals perceived transfer climate is low. The results offer new insight beyond previous findings on positive or non-significant relationship between MTL and innovative behavior. The results further show that this interactive effect is induced by motivation to transfer. Particularly, low transfer climate reduces individuals’ motivation to transfer, and individuals with high MTL have low innovative behavior when they are less motivated to transfer.

To provide a critique of Belbin's team role theory, including the provision of a re‐definition of the concept of team role and an adequate framework for relating personality to team roles. The re‐defined concept of team roles has a significant social dimension that relates it to the roles people habitually play in teams, the autonomy provided by such roles and their commitment to them. It also advocates the use of the “Big Five” model for describing individual personality differences and relating them to team role behaviour.

A revised model of team role behaviour is described, along with a brief account of the “Big Five” model of personality, and findings are presented that relate team role behaviour to three sets of variables, namely, personality, team role expectations and team role orientation, including autonomy and commitment.

Team role behaviour is described using both self‐assessments and aggregated assessments by others derived from instruments using Likert‐type scales. Information is presented showing the relationship between these measures of team role behaviour and three sets of variables, namely, personality, team role expectations and team role orientation, including autonomy and commitment. These findings support the idea that team roles have a significant social dimension and that the “Big Five” model of personality provides a useful model for relating team role behaviour to individual personality traits.

The research does not look at a number of other issues raised by Belbin's theory of team roles, including the relationship between team composition and team effectiveness. Further research, using the measures described in the article, could be carried out to explore this relationship in actual teams, including exploring team composition in different work contexts.

The main implication of the research is that, while team role behaviour does appear to be related in part to individual personality traits, such traits are much less constraining than Belbin's theory suggests. Team role behaviour can usefully be seen, in part at least, as learned social behaviour, with individuals learning to play different roles in teams. Thus attempts to improve team effectiveness would benefit from looking more at learned behaviour (including leadership, problem solving, work organisation and interpersonal skills, as well as specialist expertise relevant to the particular team), while focusing relatively less on assessment, selection, placement and guidance.

Previous research on, and criticisms of, Belbin's team role theory have challenged it from within the discipline of psychology. This research is unique in criticising it from a more sociological perspective. It is also unique in shifting the practical focus for improving team effectiveness away from assessment, selection, placement and guidance to learned behaviour and skills.