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This conceptual study investigates the adoption of education technology (EdTech) products among college students, focusing on identifying the key factors influencing the adoption process within educational institutions. Technology integration in education has rapidly gained prominence, with EdTech offering innovative solutions to enhance teaching and learning experiences. However, understanding the determinants that affect EdTech adoption remains critical for its successful implementation and impact. This paper aims (1) to identify the factors influencing the adoption of EdTech by college students (2) to create a conceptual model that shows the connections between the elements that lead to college students adopting EdTech.

The research employed a mixed-methods approach, combining qualitative data analysis and conceptual modeling to achieve the objectives. The underlying knowledge required to create a qualitative data gathering tool was obtained through a thorough literature analysis on innovation dissemination, educational psychology and technology adoption. College students, teachers and administrators participated in semi-structured interviews, focus groups and surveys to provide detailed perspectives on their attitudes about and experiences with EdTech. The Scopus and Web of Science databases are searched for relevant information in an organized manner in order to determine the factors influencing the adoption of EdTech. Second, an extended version of the technology adoption model is adopted to develop a qualitative data-based conceptual framework to analyze EdTech adoption in the Indian context.

Overall, by highlighting the critical components that emotionally influence college students' adoption of EdTech products in educational institutions, this course adds to the body of information already in existence. The conceptual framework model serves as a roadmap for educational stakeholders seeking to leverage EdTech effectively to enrich the learning environment and improve educational outcomes. By recognizing the significance of the identified factors, academic institutions can make informed decisions to foster a climate conducive to successful EdTech integration.

A comprehensive conceptual framework model was developed based on qualitative data analysis to illustrate the interrelationships between the identified factors influencing EdTech adoption. This model presents a valuable tool for educational institutions, policymakers and EdTech developers to comprehend the complex dynamics of implementing these technological solutions.

The findings of this study demonstrated a number of important variables that affect the uptake of EdTech products in educational settings. These factors encompassed technological infrastructure, ease of use, perceived usefulness, compatibility with existing academic practices, institutional support, financial constraints and individual attitudes towards technology. Additionally, the research explored the significance of institutional preparation for embracing technological advancements as well as the influence of socio-cultural elements.

In order to estimate the value of semi-automated subject indexing in operative library catalogues, the study aimed to investigate five different automated implementations of an open source software package on a large set of Swedish union catalogue metadata records, with Dewey Decimal Classification (DDC) as the target classification system. It also aimed to contribute to the body of research on aboutness and related challenges in automated subject indexing and evaluation.

On a sample of over 230,000 records with close to 12,000 distinct DDC classes, an open source tool Annif, developed by the National Library of Finland, was applied in the following implementations: lexical algorithm, support vector classifier, fastText, Omikuji Bonsai and an ensemble approach combing the former four. A qualitative study involving two senior catalogue librarians and three students of library and information studies was also conducted to investigate the value and inter-rater agreement of automatically assigned classes, on a sample of 60 records.

The best results were achieved using the ensemble approach that achieved 66.82% accuracy on the three-digit DDC classification task. The qualitative study confirmed earlier studies reporting low inter-rater agreement but also pointed to the potential value of automatically assigned classes as additional access points in information retrieval.

The paper presents an extensive study of automated classification in an operative library catalogue, accompanied by a qualitative study of automated classes. It demonstrates the value of applying semi-automated indexing in operative information retrieval systems.

Sarcasm is a linguistic expression that usually carries the opposite meaning of what is being said by words, thus making it difficult for machines to discover the actual meaning. It is mainly distinguished by the inflection with which it is spoken, with an undercurrent of irony, and is largely dependent on context, which makes it a difficult task for computational analysis. Moreover, sarcasm expresses negative sentiments using positive words, allowing it to easily confuse sentiment analysis models. This paper aims to demonstrate the task of sarcasm detection using the approach of machine and deep learning.

For the purpose of sarcasm detection, machine and deep learning models were used on a data set consisting of 1.3 million social media comments, including both sarcastic and non-sarcastic comments. The data set was pre-processed using natural language processing methods, and additional features were extracted and analysed. Several machine learning models, including logistic regression, ridge regression, linear support vector and support vector machines, along with two deep learning models based on bidirectional long short-term memory and one bidirectional encoder representations from transformers (BERT)-based model, were implemented, evaluated and compared.

The performance of machine and deep learning models was compared in the task of sarcasm detection, and possible ways of improvement were discussed. Deep learning models showed more promise, performance-wise, for this type of task. Specifically, a state-of-the-art model in natural language processing, namely, BERT-based model, outperformed other machine and deep learning models.

This study compared the performance of the various machine and deep learning models in the task of sarcasm detection using the data set of 1.3 million comments from social media.

Self-regulated learning (SRL) is crucial for successful learning and lifelong learning in today’s rapidly changing world, yet research has shown that many learners need support for SRL. Recently, learning analytics has offered exciting opportunities for better understanding and supporting SRL. However, substantial endeavors are still needed not only to detect learners’ SRL processes but also to incorporate human values, individual needs and goals into the design and development of self-regulated learning analytics (SRLA). This paper aims to examine the challenges that lifelong learners faced in SRL, their needs and desirable features for SRLA.

This study triangulated data collected from three groups of educational stakeholders: focus group discussions with lifelong learners (n = 27); five teacher interviews and four expert evaluations. The groups of two or three learners discussed perceived challenges, support needs and willing-to-share data contextualized in each phase of SRL.

Lifelong learners in professional development programs face challenges in managing their learning time and motivation, and support for time management and motivation can improve their SRL. This paper proposed and evaluated a set of design principles for SRLA.

This paper presents a novel approach for theory-driven participatory design with multistakeholders that involves integrating learners, teachers and experts’ perspectives for designing SRLA. The results of the study will answer the questions of how learners’ voices can be integrated into the design process of SRLA and offer a set the design principles for the future development of SRLA.

Research into responsible management education has largely focused on the merits, attributes, and transformation opportunities to enhance responsible business school education aims. As such, a prominent part of the literature has occupied itself with examining if responsible management modules are inherently considered a non-crucial element of the curriculum and determining the extent to which business schools have introduced such learning content into their curriculum. However, there has been scant research into how to apply novel teaching approaches to engage students and promote responsible management education endeavours. As such, this paper seeks to address this gap through the development of a teaching framework to support educators in designing effective learning environments focused on responsible management education. We draw on constructivist learning theories and Lego Serious Play (LSP) as a learning enhancement approach to develop a pedagogical framework titled The Educator's LSP Journey. LSP is chosen due to its increasing application in learning environments to help promote critical discourse, and engage with highly complex problems, whether these are social, economic, environmental, or organisational. Therefore, this paper contributes to the responsible management education discourse by providing educators with a practical methodology to support student engagement and co-creation of knowledge by fostering exploratory learning environments and enriching the practices of active learning communities.

This study aims to explore how the boundary between science and society can be addressed to support the transformation of higher education towards sustainable development (HESD) in the sense of the whole institution approach. It analyses students’ learning experiences in self-led sustainability projects conducted outside formal curricula to highlight their potential contribution to HESD. The students’ projects are conceived as learning spaces in “sustainability-oriented ecologies of learning” (Wals, 2020) in which five learning dimensions can be examined.

Using an iterative, grounded-theory-inspired qualitative approach and sensitising concepts, 13 in-depth semi-structured interviews were conducted exploring students’ learning experiences. Interviews were categorised in MAXQDA and analysed against a literature review.

Results revealed that students’ experiences of non-formal learning in self-led projects triggered deep learning and change agency. Trust, social cohesion, empowerment and self-efficacy were both results and conditions of learning. Students’ learnings are classified according to higher education institutions’ (HEIs) sustainability agendas, providing systematised insights for HEIs regarding their accommodative, reformative or transformative (Sterling, 2021) path to sustainable development.

The education for sustainable development (ESD) debate focuses mainly on ESD competences in formal settings. Few studies explore students’ learnings where formal and non-formal learning meet. This article investigates a space where students interact with different actors from society while remaining rooted in their HEIs. When acting as “change agents” in this hybrid context, students can also become “boundary agents” helping their HEIs move the sustainability agenda forward towards a whole institution approach. Learning from students’ learnings is thus proposed as a lever for transformation.

The purpose of this study is to review literature on the relationship between leadership and workplace learning, to critically analyze and discuss findings and to suggest future research paths based on the synthesis.

This study applied a refined literature review process leading to a selection of 40 articles, which originated from 14 internationally acclaimed journals.

When explaining leadership influence regarding individual and team learning, the concepts of role modeling behavior, relational support and negotiation of meaning is significant. If leaders provide support, show exemplary behavior and negotiate individual arrangements with employees, workplace learning development is positively affected.

Future studies should focus on empirical cases further illustrating how the leader–employee relationship is formed in practice, to further understand differences in leadership influence on employee workplace learning.

The gathered knowledge implicates that carefully designed leadership training programs and personalized work arrangements between leader and employees are beneficial for leader’s ability to influence employee workplace learning.

The reviewed studies were solely published in top management journals, which resulted in an original literature selection. This study also discusses implicit or articulated assumptions about the view of learning in the selected studies, offering additional understanding about the underlying learning views in leadership–workplace learning research.

The purpose of this study is to examine learning and employee-driven innovation (EDI) in the public sector, with a particular focus on the interplay between employee engagement and organisational conditions.

The material consists of qualitative interviews with 23 participants from three municipal sites of innovation support that participated in a national programme aiming to strengthen municipalities’ innovation work.

The study found numerous constraining organisational conditions resulting in consequential loss of employee engagement for EDI. The conclusion drawn is that employee engagement and enabling organisational conditions are central to EDI in public sector workplaces, and that incorporating EDI into municipal daily operations requires paying attention to the interplay between organisational conditions and employee engagement.

This paper provides important guidance for supporting EDI in the public sector. Implementing EDI into operations requires employee engagement to be successful. However, employees’ engagement should not be overlooked or taken for granted. A practical implication of this study is that EDI in the workplace must be encouraged by creating a learning environment that supports innovative learning in the workplace. In practice, measures should be taken to support employee engagement by creating organisational conditions that provide a more expansive learning environment to ensure the continuity and perpetuation of EDI in public sector organisations.

This paper aimed to determine the self- and co-regulation influences on the community of inquiry (CoI) for collaborative online learning.

A quantitative survey was used on a sample of (N = 626) enrolled postgraduate students in a South African Open Distance and e-Learning (ODeL) university. The measuring instruments were the CoI and the shared metacognitive surveys. Correlation and multiple regression analyses were used to determine the association and influence of self- and co-regulation on the CoI.

The results indicated that self- and co-regulation related to the CoI (teaching, cognitive and social) presences. In addition, the results revealed that self- and co-regulation influence the CoI presences. Self-regulation had the highest influence on teaching and cognitive presence, while co-regulation influenced social presence.

The study’s convenience sampling method from a single university limited the applicability of the findings to other online learning environments.

Higher educational teachers who encourage student self- and co-regulation may enhance their online teaching, cognitive and social presence when studying online. The research’s findings may be valuable to teachers to enable them to provide a more collaborative and interactive online learning environment and promote productive online communities.

This study contributes to the body of knowledge about the relationship between teaching, social and cognitive presence and self- and co-regulation within the CoI framework. Furthermore, there has also been limited research focussing on the dynamics of shared metacognition within the CoI framework in an ODeL context.

As higher education institutions (HEIs) have increasingly turned to consider sustainability over the last decade, education for sustainable development (ESD) has emerged as a way of imbuing students with the skills, values, knowledge, and attributes to live, work, and create change in societies facing complex and cross-cutting sustainability challenges. However, the question of how HEIs can actively embed ESD more broadly in and across curricula is one that continues to challenge institutions and the HE sector as a whole. While traditional teaching practices and methods associated with subject-based learning may be suitable for educating students about sustainable development, a re-orientation towards more transformational, experiential and action-oriented methods is required to educate for sustainable development. The need for educators to share their practices and learn lessons from each other is essential in this transformation.

This paper presents a selection of practical examples of how to embed a range of interactive, exploratory, action-oriented, problem-based, experiential and transformative ESD offerings into HE teaching practice and curricula. Presented by a group of academics and professional services staff at the University of Strathclyde who lead key modules and programmes in the institution’s ESD provision, this paper reflects on five approaches taken across the four faculties at Strathclyde (Humanities and Social Sciences, Science, Engineering and the Strathclyde Business School) and examines the challenges, practicalities and opportunities involved in establishing a collaborative programme of ESD.