The Institutionalization of Service-Learning at the Independent Colleges and Universities of the Gulf Coast Region

Service-learning is a high-impact educational practice that benefits the community, the university, and students (Jacoby, 2015). It also represents an educational practice that specifically supports the integration of Christian thought and action within Christian institutions (Schaffer, 2004). The purpose of the study was to determine the level of service-learning institutionalization within the faith-based, faith-affiliated, and secular institutions of the Gulf Coast Region of the United States. Utilizing an electronic version of Furco's (2009 Furco, A. (2009). Self-assessment rubric for the institutionalization of service-learning in higher education (rev. ed). Minneapolis, MN: International Center for Research on Community Engagement. [Google Scholar]) Self-Assessment Rubric for the Institutionalization of Service-Learning in Higher Education, chief academic officers at independent colleges and universities of the Gulf Coast region provided their respective institution's current stage of development on the institutionalization continuum. The majority of administrators reported scores indicative of development at the midpoint of the continuum, the Quality Building Stage. The presence of a university philosophy and mission was the most developed dimension, and community partner involvement was the least developed. This study provides data-driven, researched-based recommendations regarding the institutionalization of service-learning for Christian colleges and universities.     

EVIDENCE FOR THE EXPANDING ROLE OF CONSULTATION IN THE PRACTICE OF SCHOOL PSYCHOLOGISTS

In 2010, the National Association of School Psychologists approved the Model for Comprehensive and Integrated School Psychological Services (or the NASP Practice Model), codifying what an expanded role encompasses in the delivery of school‐based psychological services. This study examined the role and function of school psychology practitioners from three Midwestern states with different legal statues and policy initiatives related to the practice of school psychology. A total of 175 practicing school psychologists from Missouri, Illinois, and Iowa reported their actual and preferred practices and completed a self‐assessment on their knowledge of the NASP Practice Model. Results showed participants not only identified problem‐solving consultation as their most knowledgeable domain of practice but also the activity on which they spent the greatest amount of their time. Analysis by state revealed differential levels of practice devoted to consultation and intervention. Study findings are discussed relative to the potential impact and importance of state law and policy initiatives.     

Childhood Maltreatment and Early Developmental Vulnerabilities at Age 5 Years

This study examined associations between maltreatment and early developmental vulnerabilities in a population sample of 68,459 children (M_age = 5.62 years, SD = .37) drawn from the Australian state of New South Wales, using linked administrative data for the children and their parents (collected 2001–2009). Associations were estimated between (a) any maltreatment, (b) the number of maltreatment types, and (c) the timing of first reported maltreatment and vulnerability and risk status on multiple developmental domains (i.e., physical, social, emotional, cognitive, and communication). Pervasive associations were revealed between maltreatment and all developmental domains; children exposed to two or more maltreatment types, and with first maltreatment reported after 3 years of age, showed greater likelihood of vulnerability on multiple domains, relative to nonmaltreated children.     

Infusing educational technologies in the heart of the university—A systematic literature review from an organisational perspective

Educational technologies have experienced unprecedented prominence on university agendas with many institutions motivated to keep the lessons learned from the COVID-19 sparked transition with regard to online teaching. In response to this renewed interest in ensuring the longevity of educational technologies in higher education, this systematic review analysed the various organisational factors—for example, leadership, infrastructure, strategy—considered essential in the literature for the successful implementation of educational technologies. Specifically, we reviewed 1614 papers published in five prominent educational technology journals in the last decade. From this sample, we identified 47 papers that discussed organisational factors. Drawing on these studies, we constructed an organisational framework, which outlines the different organisational factors, actors and processes involved in implementing educational technologies. The identified organisational factors are structured into three main categories: (1) Leadership and Strategy, (2) Infrastructure and Resources and (3) Recognition and Motivation. Our aim was to further the scholarly understanding of the organisational layer involved in digital change as well as provide concrete recommendations for practitioners.

Practitioner notes

What is already known about this topic

• Previous research has stressed the importance of taking organisational factors such as infrastructure, leadership, strategy and staff commitment into account when implementing educational technologies.
• However, review papers have failed to systematically organise these studies to create a comprehensive understanding of the organisational factors involved in implementing educational technologies and ensuring their longevity at an institution.
• There is currently a high level of interest in how educational technologies can be implemented in the higher education landscape, as many institutions are facing the question of what lessons they can learn from the crisis and how they can continue on their path of digitalisation.

What this paper adds

• This review paper addresses a gap in our scholarly understanding of the organisational layers involved in the implementation of educational technologies in higher education institutions (HEIs).
• This paper provides a framework on organisational factors, which influence the implementation of educational technologies in HEIs.
• This review paper demonstrates that bottom-up and opinion leadership, support structures tailored to the need and time of faculty as well as recognition and incentives have the largest impact on a sustainable implementation of educational technologies in HEIs.

Implications for practice and/or policy

• Universities should create structures that enable innovation and creativity by promoting bottom-up and opinion leadership as well as shared decision-making processes as they are important for the successful implementation of educational technologies in HEIs.
• Besides providing a reliable and suitable infrastructure, institutional support and resources in terms of technical advice and training tailored to specific needs, should be in place when planning the implementation of educational technologies in HEIs.
• The additional workload instructors face when implementing digital teaching should be recognised and incentivised as it strengthens instructor engagement which is crucial for the implementation of educational technologies in HEIs.

     

Using natural language processing to support peer-feedback in the age of artificial intelligence: A cross-disciplinary framework and a research agenda

Advancements in artificial intelligence are rapidly increasing. The new-generation large language models, such as ChatGPT and GPT-4, bear the potential to transform educational approaches, such as peer-feedback. To investigate peer-feedback at the intersection of natural language processing (NLP) and educational research, this paper suggests a cross-disciplinary framework that aims to facilitate the development of NLP-based adaptive measures for supporting peer-feedback processes in digital learning environments. To conceptualize this process, we introduce a peer-feedback process model, which describes learners' activities and textual products. Further, we introduce a terminological and procedural scheme that facilitates systematically deriving measures to foster the peer-feedback process and how NLP may enhance the adaptivity of such learning support. Building on prior research on education and NLP, we apply this scheme to all learner activities of the peer-feedback process model to exemplify a range of NLP-based adaptive support measures. We also discuss the current challenges and suggest directions for future cross-disciplinary research on the effectiveness and other dimensions of NLP-based adaptive support for peer-feedback. Building on our suggested framework, future research and collaborations at the intersection of education and NLP can innovate peer-feedback in digital learning environments.

Practitioner notes

What is already known about this topic

• There is considerable research in educational science on peer-feedback processes.
• Natural language processing facilitates the analysis of students' textual data.
• There is a lack of systematic orientation regarding which NLP techniques can be applied to which data to effectively support the peer-feedback process.

What this paper adds

• A comprehensive overview model that describes the relevant activities and products in the peer-feedback process.
• A terminological and procedural scheme for designing NLP-based adaptive support measures.
• An application of this scheme to the peer-feedback process results in exemplifying the use cases of how NLP may be employed to support each learner activity during peer-feedback.

Implications for practice and/or policy

• To boost the effectiveness of their peer-feedback scenarios, instructors and instructional designers should identify relevant leverage points, corresponding support measures, adaptation targets and automation goals based on theory and empirical findings.
• Management and IT departments of higher education institutions should strive to provide digital tools based on modern NLP models and integrate them into the respective learning management systems; those tools should help in translating the automation goals requested by their instructors into prediction targets, take relevant data as input and allow for evaluating the predictions.