Learning Community Research Articles

Special issues: The roles of special issues in scholarly communication in a changing publishing landscape

AbstractThis paper aims to enhance the understanding of the role of special issues in the evolving landscape of academic publishing, offering insights for publishers, editors, guest editors, and researchers, including how new technologies influence transparency in publishing processes, open access models, and metrics for success. Based upon original analysis, the paper also discusses the importance of special issues and opportunities to support diversity, equity, and inclusivity in special issue publishing programmes. The goal is to contribute to the discussion of maintaining research integrity through special issues, acknowledging their significance in scholarly communication, while offering suggestions for the future.

Honoring the Heartwork of Indigenous Scholars Through Lived Experiences: Voices from Native Women Scholars in New Mexico

This body of knowledge provides personal narratives of the lived experiences of Native women from an all-Native doctoral cohort. Through personal narratives, we aimed to highlight the unique learning experience created by strong and beautiful Indigenous scholars who honored Native identities, engaged learning with Indigenous knowledge systems, cultivated Native educational leadership, and mentorship necessary for student success, work-life balance, and giving back to tribal communities. Creating a learning community for an all-Native cohort disrupts colonial legacies and its ongoing practices, marking a significant step toward progress and change, and illuminates the voice and presence of Native American mothers, daughters, aunties, professionals, and scholars. Our experiences as Native women scholars are interconnected with our cultural values, family and community, and traditional teachings. Entering the academy with these embodied teachings and knowledge provided a foundation to engage with Indigenous frameworks and theories, relational practices with tribes and communities, and reclaim knowledge and ways of being that create new possibilities for Native futures. These experiences provide an understanding of the joy, compassion, successes, and negotiating life in the academy among two Native women scholars.

Implementation outcomes from a randomized, controlled trial of a strategy to improve integration of behavioral health and primary care services

BackgroundIntegrating behavioral health services in primary care is challenging; a toolkit approach to practice implementation can help. A recent comparative effectiveness randomized clinical trial examined the impact of a toolkit for improving integration on outcomes for patients with multiple chronic conditions. Some aspects of behavioral health integration improved; patient-reported outcomes did not. This report evaluates the implementation strategy (Toolkit) using Proctor’s (2011) implementation outcomes model.MethodsUsing data from the 20 practices randomized to the active (toolkit strategy) arm (education, redesign workbooks, online learning community, remote coaching), we identified 23 measures from practice member surveys, coach interviews, reports, and field logs to assess Toolkit acceptability, appropriateness, feasibility, and fidelity. A practice survey score was high (met expectations) if its average was ≥ 4 on a scale 1-5; all other data were coded dichotomously, with high = 1.ResultsRegarding acceptability, 74% (14) of practices had high scores for willingness of providers and staff to use the Toolkit and 68% (13) for quality improvement teams liking the Toolkit. For appropriateness, 95% (19) of practices had high scores for the structured process being a good match and 63% (12) for the Toolkit being a good match. Feasibility, measured by Toolkit prerequisites, was scored lower by site members at project end (e.g., provider leader available as champion: 53% of practices) compared to remote coaches observing practice teams (74%). For “do-ability,” coaches rated feasibility lower for practices (e.g., completion of workbook activities: 32%) than the practice teams (68%). Fidelity was low as assessed across seven measures, with 50% to 78% of practices having high scores across the seven measures.ConclusionsExisting data from large trials can be used to describe implementation outcomes. The Toolkit was not implemented with fidelity in at least one quarter of the sites, despite being acceptable and appropriate, possibly due to low feasibility in the form of unmet prerequisites and Toolkit complexity. Variability in fidelity reflects the importance of implementation strategies that fit each organization, suggesting that further study on contextual factors and use of the Toolkit, as well as the relationship of Toolkit use and study outcomes, is needed.Trial registrationClinicalTrials.gov NCT02868983; date of registration: 08/15/2016.

Pergeseran Persepsi Budaya oleh Eksistensi Bahasa Indonesia pada Mahasiswa di Korea Selatan (2020-2024)

As time progresses, language has become one of the diplomatic tools used in the international arena. In South Korea, interest in the Indonesian language has been growing, as evidenced by the increasing number of students showing interest in learning it each year. The author identifies an urgency to monitor the perceptions formed when foreign nationals learn Indonesian—in this context, South Korean society. This research seeks to elaborate on the cultural perceptions that emerge with the presence of the Indonesian language in South Korea. The aim of this study is to understand how the construction of cultural perceptions takes shape in the minds of South Koreans when they learn Indonesian. A descriptive qualitative research method was used, with data collection techniques in the form of interviews. The findings of this study indicate a shift in cultural perceptions of Indonesia, with Indonesian language learning fostering curiosity about Indonesian culture. Ultimately, this indirectly plays a role in promoting Indonesian culture. This shift is evident both in the mindset and in social interactions within the student community learning Indonesian.

Efektivitas Komunitas Belajar dalam Meningkatkan Kualitas Guru di Sekolah

Teacher learning communities are regarded as essential means to improve education quality through collaboration and professional development. However, implementation within formal school contexts faces challenges, such as time constraints, limited structural support, and low awareness of the benefits of learning communities. This study aims to explore the effectiveness of learning communities in enhancing teacher quality in schools and to identify factors influencing teacher participation. The research method employed is a literature review, encompassing analysis of 25 articles from various relevant academic journals. Thematic analysis highlights key topics, including effectiveness, challenges, and opportunities for the development of learning communities. Findings indicate that learning communities have a positive role in enhancing teacher competence and work motivation. However, significant obstacles remain, such as lack of support from school management and limited understanding. The study also emphasizes the potential for digital technology-based learning communities, which could increase teacher participation by providing more flexible access. This study concludes that stronger policy support and further research are needed to address implementation barriers and maximize the potential of learning communities to enhance teaching quality in schools.

Access and trust in scholarly communication and scholarly publishing

Access and trust in scholarly communication and scholarly publishing

Small Group Learning Communities as a Prototype for Intersectional Black Feminist Praxis in Higher Education

Through the creation and analysis of Small Group Learning Communities (SGLC) at a predominantly White university in the U.S. South, this study investigated how SGLCs operationalize intersectional Black feminist praxis via dialogue, liberation, and ethic of caring. The racialized and gendered organizational dynamics that govern institutions of higher education make it an ideal site for such an intervention. Through an instrumental case study of 13 SGLCs, we uncover how SGLCs served interrelated, but separate purposes: (1) affirmation, mentorship, and professional development among “Black women-focused” SGLCs; and (2) epiphanies of intersectional oppression and nuanced Black girl representation in educational settings among “Black women-affirming” SGLCs. Findings suggest that SGLCs are innovative prototypes for understanding, studying, and implementing intersectional Black feminist praxis. We submit that SGLCs can advance broader social justice and liberation agendas. Moreover, findings may inform efforts to center Black women in higher education.

Beyond 'spray on' professional development: Enhancing supervision in a tertiary teaching hospital.

Short 'programmes' of professional development can have limited impact on clinical supervisors' practices. This paper reports on an innovative programme of professional development, implemented in a tertiary teaching hospital, that was designed to build clinical supervision capacity, improve the educational practice of frontline clinical supervisors and cultivate future educational leaders. The programme was a partnership venture between St. Vincent's Hospital Melbourne and the University of Melbourne. It has a three-tiered tapered design: a foundational self-paced online course; online, interprofessional learning communities; and a Graduate Certificate in Clinical Education. Participants progressed from one tier to the next, with the largest number of employees participating in the primary tier (N = 112). We adopted a utilisation-focused approach to evaluation, collecting multiple data sets across the tiers. Participants reported greater consciousness of their teaching practices, made changes to their practice, and the interprofessional learning communities allowed better integration of practical knowledge with the formal knowledge from the foundational course. Systemic outcomes included the creation of informal educator networks and the diffusion of ideas and practices within the hospital. Managers and clinical education leaders at the hospital concluded that funding this programme of professional development provided significant benefits, with a high return on investment, which may be transferable to other health care settings that place value on clinical education. For the outcomes to be sustainable, an ongoing programme of professional development needs to be built into the institution's fabric so that the resultant supervisory practices become strongly embedded in the organisational culture.

Co-clustering: A Survey of the Main Methods, Recent Trends, and Open Problems

Since its early formulations, co-clustering has gained popularity and interest both within and outside the machine learning community as a powerful learning paradigm for clustering high-dimensional data with good explainability properties. The simultaneous partitioning of all the modes of the input data tensors (rows and columns in a data matrix) is both a method for improving clustering on one mode while performing dimensionality reduction on the other mode(s), and a tool for providing an actionable interpretation of the clusters in the main mode as summaries of the features in each other mode(s). Hence, it is useful in many complex decision systems and data science applications. In this article, we survey the the co-clustering literature by reviewing the main co-clustering methods, with a special focus on the work done in the past 25 years. We identify, describe, and compare the main algorithmic categories and provide a practical characterization with respect to similar unsupervised techniques. Additionally, we try to explain why it is still a powerful tool despite the apparent recent decreasing interest shown by the machine learning community. To this purpose, we review the most recent trends in co-clustering research and outline the open problems and promising future research perspectives.

"Math is a pain!": Understanding challenges and needs of the Machine Learning community on Stack Overflow

Stack Overflow (SO) is a widely recognized online question-and-answer platform for programming, which has also fostered a substantial community dedicated to machine learning (ML), providing a space for both novices and experts to exchange ideas and find solutions to ML-related problems. However, as a relative minority of this online programming platform, research has demonstrated lower engagement in the ML community, but it remains largely unexplored to understand what hinders the engagement and contribution from ML users' perspectives. This paper presents an empirical study based on 22 hours of semi-structured interviews and 131 survey responses with users on SO and reveals the key factors that may lead to the lower response rate and extended waiting time for ML questions on SO, which includes the unique quality requirement for posting ML questions, the discrepancy between time invested and benefits gained, the dispersed nature of the ML community across various platforms and the desired improvement for SO. Moreover, the qualitative study reveals a declining friendliness in SO's culture over time; the subsequent quantitative study corroborates that newcomers frequently encounter stress when posting and answering ML questions, even though this stress diminishes with increased experience. Additionally, we also explored the potential influence of generative AI tools (e.g., ChatGPT) on online question-and-answer platforms, specifically focusing on ML Q&A. We hope the results of this study can pave the way for enhancing the experience of ML users on online platforms, ultimately facilitating improved knowledge exchange and collaboration within the ML domain.

Critical Assessment of Protein Engineering (CAPE): A Student Challenge on the Cloud.

The success of AlphaFold in protein structure prediction highlights the power of data-driven approaches in scientific research. However, developing machine learning models to design and engineer proteins with desirable functions is hampered by limited access to high-quality data sets and experimental feedback. The Critical Assessment of Protein Engineering (CAPE) challenge addresses these issues through a student-focused competition, utilizing cloud computing and biofoundries to lower barriers to entry. CAPE serves as an open platform for community learning, where mutant data sets and design algorithms from past contestants help improve overall performance in subsequent rounds. Through two competition rounds, student participants collectively designed >1500 new mutant sequences, with the best-performing variants exhibiting catalytic activity up to 5-fold higher than the wild-type parent. We envision CAPE as a collaborative platform to engage young researchers and promote computational protein engineering.

A Hybrid Quantum-Classical Model for Stock Price Prediction Using Quantum-Enhanced Long Short-Term Memory

The stock markets have become a popular topic within machine learning (ML) communities, with one particular application being stock price prediction. However, accurately predicting the stock market is a challenging task due to the various factors within financial markets. With the introduction of ML, prediction techniques have become more efficient but computationally demanding for classical computers. Given the rise of quantum computing (QC), which holds great promise for being exponentially faster than current classical computers, it is natural to explore ML within the QC domain. In this study, we leverage a hybrid quantum-classical ML approach to predict a company’s stock price. We integrate classical long short-term memory (LSTM) with QC, resulting in a new variant called QLSTM. We initially validate the proposed QLSTM model by leveraging an IBM quantum simulator running on a classical computer, after which we conduct predictions using an IBM real quantum computer. Thereafter, we evaluate the performance of our model using the root mean square error (RMSE) and prediction accuracy. Additionally, we perform a comparative analysis, evaluating the prediction performance of the QLSTM model against several other classical models. Further, we explore the impacts of hyperparameters on the QLSTM model to determine the best configuration. Our experimental results demonstrate that while the classical LSTM model achieved an RMSE of 0.0693 and a prediction accuracy of 0.8815, the QLSTM model exhibited superior performance, achieving values of 0.0602 and 0.9736, respectively. Furthermore, the QLSTM outperformed other classical models in both metrics.

Educational teams: building professional and organizational learning communities

Quality education that prepares young people to think and make ethically responsible decisions seems to be the only sustainable solution to respond to today’s global challenges. However, this capacity to learn requires a new school grammar, through the creation of conditions, structures and cultures that promote relationships and synergies capable of transforming teachers and school leaders into creative learners and facilitators of learning. In this sense, this study sought to understand the effects of a change in the organizational dynamics of a school, the organization of teaching by educational teams, on the individual, collective and organizational learning of this school organization. To this end, we adopted a fundamentally qualitative research approach, which we operationalized through a case study, based on a quali-quanti approach. We combined a descriptive statistical analysis of two questionnaires with a content analysis of interviews, focus discussion groups, and field notes from classroom observations and educational team meetings. The analysis of all these data points to the fact that effective change in schools and education that promotes deep learning for teachers and, consequently, for students and the organization, requires joint and articulated action not only at the level of structure and leadership, but also at the level of beliefs and school cultures.

‘People-in-environments’ taking the classroom to the community: evaluation of a collaborative problem-based community learning model for social work students

ABSTRACT This paper reports on an innovative community-based ‘People-in-Environments’ learning model designed to support social work students in their development of ‘Readiness for Practice’ in an English urban metropolis. This experiential model challenges students to think differently about the meaning of communities, community resources, and their own assumptions, whilst enhancing critical thinking, self-awareness, reflexivity, and the integration of anti-oppressive values. A central element involves engaging students in embodied, problem-based, confluent learning by critically responding to scenarios using real-life circumstances and visiting diverse community locations. Drawing on qualitative questionnaires, focus groups, and anecdotal data from selected student cohorts 2018–2023, six learning themes are presented: Enhanced Knowledge, Deeper Learning; Examining the Familiar Unfamiliar; What Does Community Mean; Person-In-Environment, Self-In-Environment; A Different Kind of Empathy, and Group Work Challenges. Students’ narratives emphasize the significance of embodied, emplaced learning in unfamiliar settings. Reflexively analyzing thoughts, emotions, and materiality enhanced understanding and skill development. This model facilitates nuanced appreciation of what community means, both the intersections of geographical locales with identities and attachments, and the importance of understanding community attributes, strengths, risks, and resources. The paper concludes with suggestions for creative expansion through community mapping in placements and integrative learning in social work curricula.

This Course Is Ungraded: The Impact of Ungrading Practices on Students and Their Instructor

The researcher studied ungrading practices in an introductory course on emergent language and literacy in the early years for undergraduate students interested in pursuing careers as classroom teachers at an urban public college in the northeast United States. Based on the Scholarship of Teaching and Learning (SoTL), the researcher applied self-efficacy and reflective teaching theories to address how a course designed with ungrading practices impacted students' self-efficacy and the researcher as a reflective teacher. The researcher used mixed methods to analyze data. Findings show that students' participation presented a calibration of dynamic interaction between personal, behavioral, and environmental factors. Findings also show how integral it was to build a supportive learning community to develop students' self-efficacy. Ungrading practices created conditions for a humanizing pedagogy. The researcher applied an ethics of care that diminished power over students and instead emphasized power with students. This shift in power enabled dialogic discourse that informed responsive teaching and reflective practice. Rather than best practices, a course designed with ungrading practices is deeply contextual and requires an inquiry stance.

A Roadmap of Explainable Artificial Intelligence: Explain to Whom, When, What and How?

Explainable artificial intelligence (XAI) has gained significant attention, especially in AI-powered autonomous and adaptive systems (AASs). However, a discernible disconnect exists among research efforts across different communities. The machine learning community often overlooks “explaining to whom,” while the human-computer interaction community has examined various stakeholders with diverse explanation needs without addressing which XAI methods meet these requirements. Currently, no clear guidance exists on which XAI methods suit which specific stakeholders and their distinct needs. This hinders the achievement of the goal of XAI: providing human users with understandable interpretations. To bridge this gap, this paper presents a comprehensive XAI roadmap. Based on an extensive literature review, the roadmap summarizes different stakeholders, their explanation needs at different stages of the AI system lifecycle, the questions they may pose, and existing XAI methods. Then, by utilizing stakeholders’ inquiries as a conduit, the roadmap connects their needs to prevailing XAI methods, providing a guideline to assist researchers and practitioners to determine more easily which XAI methodologies can meet the specific needs of stakeholders in AASs. Finally, the roadmap discusses the limitations of existing XAI methods and outlines directions for future research.

Applicability of ChatGPT in Online Collaborative Learning: Evidence Based on Learning Outcomes

The study delves into the impact of ChatGPT, a generative AI technology, on online collaborative learning (OCL) outcomes in educational settings. With a focus on knowledge activation and critical thinking, this research involved thirty university students from various disciplines, divided into experimental (ChatGPT) and control (traditional online search) groups. The research process involved controlled experiment and semi-structured interviews. Results revealed a significant disparity in knowledge activation levels between the experimental and control groups, showcasing the efficacy of ChatGPT in enhancing students' engagement with challenging concepts. Furthermore, the study underscored ChatGPT's role in fostering critical thinking skills, thereby enriching the learning experience and promoting deeper understanding among participants. The findings shed light on the potential of AI technologies, like ChatGPT, to serve as virtual tutors, guiding learners through problem-solving tasks and facilitating metacognitive skills development. By facilitating collaborative interactions and peer-to-peer learning, ChatGPT emerged as a valuable mediator in online discussions and group activities, encouraging active participation and knowledge sharing within learning communities. The research contributes to the evolving landscape of education technology, emphasizing the significance of AI-driven tools in augmenting educational practices and enhancing student learning outcomes. This study paves the way for further exploration of AI integration in educational contexts, offering insights into the transformative potential of generative AI technologies like ChatGPT.

Examining Finnish teachers’ professional agency in the professional community in curriculum reform / Análisis de la agencia profesional de los docentes finlandeses en la comunidad profesional en la reforma curricular

Reforming curriculum is potentially a powerful tool for school development. Harnessing this potential calls for teacher engagement with colleagues for active professional learning, i.e., teachers’ professional agency. However, promoting teachers’ ownership and active involvement in curriculum-making provides a challenge for all levels of policy interpretation. Furthermore, variation in how teachers perceive a curriculum reform, its impact in schools and their agency within the reform are likely to occur. Yet, we still know little about this variation. We address this gap identified in prior literature by examining the individual variation and relationships within and between teachers’ sense of professional agency, perceptions of the large-scale curriculum reform impact and perceptions of overall success of the reform on a national level in the Finnish context. Teachers ( N = 1,531) from 74 schools in Finland participated in the study. Relationships between variables were examined using t-tests and ANOVAs, and latent profile analysis was used to identify professional agency profiles. The results showed that perceptions of reform impact, reform overall success and professional agency varied partly according to gender, school size, school type and teaching experience. Four profiles were identified: High sense of professional agency, Reduced collective efficacy, Active help seekers and Commitment to mutual agreement. High perceived school impact and overall reform success were linked to strong professional agency. The findings imply that development work in the context of curriculum reform can be facilitated by agentic learning in the professional community.

Quality Learning Environment and Community Participation: Report on a Literacy Boost Project in Indonesia

This article reports the outcomes of community participation and quality learning education resulting from the MEMBACA II project. MEMBACA (Making Early Matter through Books and Community Action) is a literacy project that targets young children in Indonesia. The MEMBACA project includes teacher training, community reading activities, and age-appropriate local language material creation to support emergent literacy skills among early-grade children. These skills include letter awareness, single-word reading of most used words, reading fluency, reading accuracy, and reading comprehension. 31 participants involved in this study, purposively selected to be interviewed through the phone. The thematic analysis found that a better learning environment was produced, a code of conduct is now available in schools, and it has helped in mainstreaming a more positive disciplining approach to teachers. This study has also identified that villages are now allocating their village fund for supporting literacy activities, although the process has experienced a setback in the year 2020 due to the pandemic. Further, while teachers’ capacities were increased due to various activities, learning environments are still troubled by poor infrastructures and a lack of community awareness. Engagement of communities and local government were mostly improved, with major problem lies in the continuity of the literacy-related activities, affected by various factors including poor advocacy and COVID-19 pandemic.

A mixed-methods survey and focus group study to understand researcher and clinician preferences for a Journal transparency Tool

Transparency within biomedical research is essential for research integrity, credibility, and reproducibility. To increase adherence to optimal scientific practices and enhance transparency, we propose the creation of a journal transparency tool (JTT) that will allow users to obtain information about a given scholarly journal’s operations and transparency policies. This study is part of a program of research to obtain user preferences to inform the proposed JTT. Here, we report on our consultation with clinicians and researchers. This mixed-methods study was conducted in two parts. The first part involved a cross-sectional survey conducted on a random sample of authors from biomedical journals. The survey asked clinicians and researchers about the inclusion of a series of potential scholarly metrics and user features in the proposed JTT. Quantitative survey items were summarized with descriptive statistics. Thematic content analysis was employed to analyze text-based responses. Subsequent focus groups used survey responses to further explore the inclusion of items in the JTT. Items with less than 70% agreement were used to structure discussion points during these sessions. Participants voted on the use of user features and metrics to be considered within the journal tool after each discussion. Thematic content analysis was conducted on interview transcripts to identify the core themes discussed. A total of 632 participants (5.5% response rate) took part in the survey. A collective total of 74.7% of respondents found it either ‘occasionally, ‘often’, or ‘almost always’ difficult to determine if health information online is based on reliable research evidence. Twenty-two participants took part in the focus groups. Three user features and five journal tool metrics were major discussion points during these sessions. Thematic analysis of interview transcripts resulted in six themes. The use of registration was the only item to not meet the 70% threshold after both the survey and focus groups. Participants demonstrated low scholarly communication literacy when discussing tool metric suggestions. Our findings suggest that the JTT would be valuable for both researchers and clinicians. The outcomes of this research will contribute to developing and refining the tool in accordance with researchers and clinicians.