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Based on her membership in the Educational Research Group (The Danish name for the group is UFO group – Educational Research = UddannelsesFOrskning) at Copenhagen Business School, the author discusses learning as a paradox and how learning processes may be supported in adult learners through the use of social media enhanced learning platforms in the classroom. The chapter begins by looking at three paradigms in regard to adult learners’ learning processes and discusses how these paradigms may be put to use in connection with the application of Web 2.0 tools in the classroom. The chapter discusses the changing roles of educators and learners in connection with these new tools, just as it discusses which tools may be particularly useful in certain conditions. It ends by mentioning a number of concrete cases where the use of (first) an e-learning platform and (later on) social media enhanced learning tools has had an impact on student learning. The chapter additionally links university learning to organizational learning, since processes and principles are transferable between the two.

The purpose of this study is to identify the main perspectives and trends in educational data mining (EDM) in the e-learning environment from a managerial perspective.

This paper proposes a systematic literature review to identify the main perspectives and trends in EDM in the e-learning environment from a managerial perspective. The study domain of this review is restricted by the educational concepts of e-learning and management. The search for bibliographic material considered articles published in journals and papers published in conferences from 1994 to 2023, totaling 30 years of research in EDM.

From this review, it was observed that managers have been concerned about the effectiveness of the platform used by students as it contains the entire learning process and all the interactions performed, which enable the generation of information. From the data collected on these platforms, there are improvements and inferences that can be made about the actions of educators and human tutors (or automatic tutoring systems), curricular optimization or changes related to course content, proposal of evaluation criteria and also increase the understanding of different learning styles.

This review was conducted from the perspective of the manager, who is responsible for the direction of an institution of higher education, to assist the administration in creating strategies for the use of data mining to improve the learning process. To the best of the authors’ knowledge, this review is original because other contributions do not focus on the manager.

This paper aims to clarify the relationship between the student’s study time and the learning process in the higher education system by adapting the total quality management (TQM) principles-process approach. Contrary to Deming’s (1982) constancy of purpose to improve the learning process, some students in higher education postpone their studies till the last few weeks of an examination.

The paper opted for an experimental study with three different classes of business school students. The experimental research question was “Do student’s study time (massed or distributed spacing) has an impact on the learning process?”

Results indicated that students in the “Strictly supervised study time” group improved on their learning process more than the “Not Strictly Supervised study time” and the control group. It is important for students to manage their own learning activities and follow a regular study time and constantly improve their learning process as proposed by Deming (1982).

This study used restricted to undergraduate business school students in a university in Ghana, and may not necessarily be applicable universally. One other limitation was that the authors did not control for lecturer’s expectancies and how these may have influenced the students’ learning process. Another potential limitation was that TQM was the only subject area used for this study.

This objective of the study is to use the TQM principles of process approach, the Learning Theory and the Learning Strategies from the Motivated Strategies for Learning Questionnaire (MSLQ) – Pintrich et al. (1991) – to support theoretical and practical implications of the relationship between the student’s study time and the learning process. The results imply that students must take a more active role in their learning by having a regular study time.

Currently, to the best of the authors’ knowledge, there are not many experiment-based studies on a student’s study time using the MSLQ-Pintrich et al. (1991) approach. This study contributes to the literature by examining how a student’s study time (massed or distributed spacing) has an impact on the learning process.

This study investigated the impact of the virtualization requirements of the learning process on students' satisfaction and their intention to continue using online learning.

A research model was developed using the process virtualization theory (PVT); it was validated empirically using data obtained from an online questionnaire-based survey of 489 undergraduate students.

The main results support the role of representation for sensory requirements, sensory requirements, reach, representation for relationship requirements and relationship requirements on shaping students' satisfaction, which all also have a significant influence on students' intention to continue using online learning. Relevance factors are responsible for 61.6% of the variance in students' satisfaction and 83.6% of the variance in their intention to continue using online learning. However, neither the synchronism requirements nor the identification and control requirements had a significant effect on students' satisfaction or on their intention to continue using online learning.

The present research focused on PVT in an online learning context; consequently, a new set of factors that influenced students' satisfaction with and intentions to continue using online learning was empirically tested for the first time. This research contributes to the literature on information systems because it advances the generalizability and applicability of the PVT in a new context and new cultural setting. Moreover, the research apprises researchers and practitioners of new factors, which should be understood and fulfilled to make virtual learning equivalent to the face-to-face learning experience.

Students develop knowledge through an ongoing process of learning embodied in their daily experiences. As citizens, they develop an identity in their communities as they build relationships through recurrent interactions, thus constructing citizenship by strengthening stable interactions. This paper aims to examine the development of student active citizenship within a Jean Monnet module summer school that uses a participative approach and experiential learning.

The research provides a multi-level systems perspective on the learning experience in a Jean Monnet module. It combines state-of-the-art analysis of the Jean Monnet modules reports, analysis of a selected module’s activities and delayed participants feedback analysis. The methodology addresses complexity at multiple levels and leaves sufficient variance to invite readers to test the approaches themselves.

First, opportunities and gaps in the development of active citizen abilities were identified within the Jean Monnet modules. Second, it was established that the use of a participative approach and experiential learning aligned activities in the learning process yielded positive results in participant engagement. Third, long-term effects in the form of an improved understanding of active citizenship and the execution of activities in real life were also observed. The authors point to the need for active communication in the development of a full-cycle experiential learning process. Additionally, the multi-level monitoring model contributed positively towards the continual improvement of the learning process, and thus, provided a learning experience for teachers.

The research is limited regarding the clear articulation of the research results, rendering comparison with other learning experience reports challenging.

For lecturers, the importance of integrating the participative approach into the student learning process is documented; the effects of experience learning on students’ active participation are presented; and the importance of systems perspective on multiple aspects of the learning process is reinforced. For students, an example of the importance of being active in the learning process and using available resources is provided. For policymakers, the paper attests to the importance of learning programmes expanding the limitations of the regular curricula and the need to support additional programmes and the benefits of a participative approach and experience learning in the process of developing active citizens.

The authors point to the need for authentic situational-context experience and active communication in the learning process. Additionally, the authors provide an example of systems investigation of the learning process.

The paper identifies the gap between the Jean Monnet modules and active citizen abilities and provides a potential approach towards reducing them. It also provides a multi-level method for monitoring and adjusting the learning process.

Case-based classroom teaching-learning process (hereafter, case method) has provided a very productive teaching-learning environment for a long time. In the case method, students are expected to meet some prerequisites, such as reading and analyzing the case in advance, listening to the classroom discussion and actively participating in the discussion. However, it is frequently reported in Indian business schools that students do not prepare the assigned case before the scheduled class. The under-preparation of cases results in low-quality discussion, high absenteeism, passive attitude and lack of energy.

This study modeled the case method using an IGEO (input-guide-enablers-output, commonly used in any process modeling) framework to identify challenges in the case-based classroom teaching-learning process. A novel customized classroom teaching-learning process called the EPDE (explain, practice, discuss, explore) method replaced the case method. These two teaching-learning processes were used for teaching two groups of MBA students.

The novel EPDE method effectively addressed the case method challenges. It resulted in better learning outcomes in the Indian B-school context.

The purpose of the study is to examine the impact of an alternative to the traditional case study method in a college classroom among MBA students. Two points make this study original and unique: (1) The IGOE process modeling framework is used to model teaching-learning processes, such as the case and EPDE methods. Using IGOE for teaching-learning processes is unique and is not available in the literature and (2) the EPDE method is a novel concept.

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.

Fishbein and Ajzen's 1975 Theory of Reasoned Action (TRA), updated by Ajzen and Fishbein in 1980, is advanced in this paper as an appropriate theory for measuring student's intentions to adopt deep or surface processing and to adopt specific learning strategies. TRA is a decision theory that explains motivation by emphasising the specific processes that individuals use to make choices. TRA captures an individual's motivation by using the concept of intention to perform a behaviour. A TRA model was constructed based on a four‐latent‐variable (deep, surface, strategic and intention) framework and empirically assessed for model data fit. The survey items showed loadings on the constructs of deep, surface and strategic processing under this framework, indicating strong construct validity for the three learning factors. The TRA model was found to strongly positively influence the adoption of the deep processing construct, and to strongly negatively influence the adoption of the surface processing construct. In addition, it was found to strongly positively influence the adoption of positive learning strategies and weakly discourage the use of negative learning strategies.

With the advent of the internet and communication technology, the penetration of e-learning has increased. The digital data being created by the educational and research institutions is also on the ascent. The growing interest in recent years toward big data, educational data mining and learning analytics has motivated the development of new analytical ways and approaches and advancements in learning settings. The need for using big data to handle, analyze this large amount of data is prime. This trend has started attracting the interest of educational institutions which have an important role in the development skills process and the preparation of a new generation of learners. “A real revolution for education,” it is based on this kind of terms that many articles have paid attention to big data for learning. How can analytics techniques and tools be so efficient and become a great prospect for the learning process? Big data analytics, when applied into teaching and learning processes, might help to improvise as well as to develop new paradigms. In this perspective, this paper aims to investigate the most promising applications and issues of big data for the design of the next-generation of massive e-learning. Specifically, it addresses the analytical tools and approaches for enhancing the future of e-learning, pitfalls arising from the usage of large data sets. Globally, this paper focuses on the possible application of big data techniques on learning developments, to show the power of analytics and why integrating big data is so important for the learning context.

Big data has in the recent years been an area of interest among innovative sectors and has become a major priority for many industries, and learning sector cannot escape to this deluge. This paper focuses on the different methods of big data able to be used in learning context to understand the benefits it can bring both to teaching and learning process, and identify its possible impact on the future of this sector in general. This paper investigates the connection between big data and the learning context. This connection can be illustrated by identifying the several main analytics approaches, methods and tools for improving the learning process. This can be clearer by the examination of the different ways and solutions that contribute to making a learning process more agile and dynamic. The methods that were used in this research are mainly of a descriptive and analytical nature, to establish how big data and analytics methods develop the learning process, and understand their contributions and impacts in addressing learning issues. To this end, authors have collected and reviewed existing literature related to big data in education and the technology application in the learning context. Authors then have done the same process with dynamic and operational examples of big data for learning. In this context, the authors noticed that there are jigsaw bits that contained important knowledge on the different parts of the research area. The process concludes by outlining the role and benefit of the related actors and highlighting the several directions relating to the development and implementation of an efficient learning process based on big data analytics.

Big data analytics, its techniques, tools and algorithms are important to improve the learning context. The findings in this paper suggest that the incorporation of an approach based on big data is of crucial importance. This approach can improve the learning process, for this, its implementation must be correctly aligned with educational strategies and learning needs.

This research represents a reference to better understanding the influence and the role of big data in educational dynamic. In addition, it leads to improve existing literature about big data for learning. The limitations of the paper are given by its nature derived from a theoretical perspective, and the discussed ideas can be empirically validated by identifying how big data helps in addressing learning issues.

Over the time, the process that leads to the acquisition of the knowledge uses and receives more technological tools and components; this approach has contributed to the development of information communication and the interactive learning context. Technology applications continue to expand the boundaries of education into an “anytime/anywhere” experience. This technology and its wide use in the learning system produce a vast amount of different kinds of data. These data are still rarely exploited by educational practitioners. Its successful exploitation conducts educational actors to achieve their full potential in a complex and uncertain environment. The general motivation for this research is assisting higher educational institutions to better understand the impact of the big data as a success factor to develop their learning process and achieve their educational strategy and goals. This study contributes to better understand how big data analytics solutions are turned into operational actions and will be particularly valuable to improve learning in educational institutions.

The purpose of this paper is to describe a computer‐assisted learning experience in operations management (OM) higher education that entailed the development of interactive learning software, its evaluation in an experimental environment and the formal analysis of the teaching method's influence on student perceptions.

The software design follows the constructivist focus based on widely‐accepted educational technology principles. Objective tests of knowledge and subjective appraisal of the learning process were used in the experiment to compare two educational scenarios (computer‐assisted learning and on‐site class). Students' perceptions of the software's technical and teaching features are also analyzed.

The study shows that the teaching method can significantly affect students' perceptions of the learning process. The findings also confirm the pedagogical effectiveness of the software that was designed and that information communication technologies (ICT)‐based methods are an alternative to traditional methods used in OM education.

The experiment involved strict control over various potential threats to validity. From a statistical point‐of‐view, the conclusions can only be generalized in the population analyzed. Nevertheless, the features of the software and the student profile allow the main conclusions to be generalized to other OM environments.

The use and evaluation of interactive software in OM educational environments are reflected on, with emphasis on the influence that the teaching methodology has on students' attitudes to the learning process. It is of interest for researchers interested in improving teaching through the use of ICT.

There are very few studies on interactive self‐learning software for OM and its effects on student perceptions. This paper is a new contribution to this field.