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The current predominant delivery format resulting from digitization is PDF, which is not appropriate for the blind, partially sighted and people who read on mobile devices. To meet the needs of both communities, as well as broader ones, alternative file formats are required. With the findings of the eBooks-On-Demand-Network Opening Publications for European Netizens project research, this study aims to improve access to digitized content for these communities.

In 2022, the authors conducted research on the digitization experiences of 13 EODOPEN partners at their organizations. The authors distributed the same sample of scans in English with different characteristics, and in accordance with Web content accessibility guidelines, the authors created 24 criteria to analyze their digitization workflows, output formats and optical character recognition (OCR) quality.

In this contribution, the authors present the results of a trial implementation among EODOPEN partners regarding their digitization workflows, used delivery file formats and the resulting quality of OCR results, depending on the type of digitization output file format. It was shown that partners using the OCR tool ABBYY FineReader Professional and producing scanning outputs in tagged PDF and PDF/UA formats achieved better results according to set criteria.

The trial implementations were limited to 13 project partners’ organizations only.

This research paper can be a valuable contribution to the field of massive digitization practices, particularly in terms of improving the accessibility of the output delivery file formats.

Municipalities, as the front lines of service delivery, use websites as one of the tools to communicate information to the public. While it is considered a record, many organisations, including municipalities, do not manage websites as such. This study aims to explore the archiving of websites as records in the municipalities of KwaZulu-Natal (KZN) Province in South Africa by using the web archiving life cycle model.

This study used a mixed-methods research with an explanatory design, with quantitative data collected first through content analysis of websites and qualitative data collected through interviews. Researchers used multilevel sampling, first quantitatively analysing all available websites of the municipalities (52) in KZN, and then qualitatively selecting only records managers, information managers, web administrators, communication managers and website managers or designers from municipalities because of their understanding and involvement with websites in some way.

This study established that some records on municipal websites are often in paper format in record-keeping systems, whereas others are born digital and are not captured in the systems. Municipalities lack a dedicated web online harvesting tool as well as an archiving policy or strategy to guide website archiving. Furthermore, municipalities placed a high reliance on service providers to keep their websites operational.

It became clear during the interviews that most of the participants were unfamiliar with web archiving. As a result, only 12 of the 56 selected participants from the municipalities provided the required information in relation to the current study as others could not provide answers. Data for other participants were not analysed.

Due to a lack of infrastructure for ingesting digital records into archival custody, a framework for harvesting web content of value is proposed both internally in municipalities and externally to an archive repository.

This study aims to introduce management students to climate change by providing them with an opportunity to address it in their own lives, through a class exercise.

An in-class exercise was designed, carried out and described in this study. Student teams were assigned different questions on how to address major causes of climate change. Each team did research to generate answers, and ranked their answers based on the speed of implementation. Teams reported their answers to the class. The instructor facilitated a debriefing session, during which all responses were ranked with respect to other variables, including cost savings, time savings and lifestyle fit. This exercise uses few resources and can be adapted to different time lengths and teaching/learning formats (e.g. on-ground, virtual, asynchronous online).

This exercise can help students to gain an understanding of climate change and its causes and complexities. Students learn how to implement a diverse set of personal actions to mitigate climate change; these can start in the present and continue throughout their lives. In addition, this exercise may help students to make the leap from individual climate change mitigation practices to organizational and societal practices, when they are in the position to do so as future leaders.

Although classes, exercises, and assignments ask management students to consider issues such as climate change at the organizational level, this individual-level exercise can allow students with limited organizational experience to engage more quickly with climate change and better understand organizational and societal implications in the future. That is, if students first understand how to address climate change in their own lives, they may more effectively transfer and apply that understanding at organizational and societal levels and ultimately synthesize solutions to address climate change in the world.

This study aims to identify effective behaviors in labor-management negotiation (LMN) and, on that basis, derive overarching psychological principles of successful negotiation in this important context. These empirical findings are used to develop and test a comprehensive negotiation training program.

Twenty-seven practitioners from one of the world’s largest labor unions were interviewed to identify the requirements of effective LMN, resulting in 796 descriptions of single behaviors from 41 negotiation cases.

The analyses revealed 13 categories of behaviors critical to negotiation success. The findings highlight the pivotal role of the union negotiator by illustrating how they lead the negotiations with the other party while also ensuring that their own team and the workforce stand united. To provide guidance for effective LMN, six psychological principles were derived from these behavioral categories. The paper describes a six-day training program developed for LMN based on the empirical findings of this study and the related six principles.

This paper has three unique features: first, it examines the requirements for effective LMN based on a systematic needs assessment. Second, by teaching not only knowledge and skills but also general psychological principles of successful negotiation, the training intervention is aimed at promoting long-term behavioral change. Third, the research presents a comprehensive and empirically-based training program for LMN.

Key to transnational higher education (HE) cooperation is building trust to allow for seamless recognition of studies. Building on the Tuning Educational Structures initiative (2001) and lessons learnt from the Organisation for Economic Cooperation and Development (OECD)-Assessment of Learning Outcomes in Higher Education (AHELO) feasibility study, this paper offers a sophisticated approach developed by the European Union (EU)-co-financed project Measuring and Comparing Achievements of Learning Outcomes in Europe (CALOHEE). These evidence the quality and relevance of learning by applying transparent and reliable indicators at the overarching and disciplinary levels. The model results allow for transnational diagnostic assessments to identify the strength and weaknesses of degree programmes.

The materials presented have been developed from 2016 to 2023, applying a bottom-up approach involving approximately 150 academics from 20+ European countries, reflecting the full spectrum of academic fields. Based on intensive face-to-face debate and consultation of stakeholders and anchored in academic literature and wide experience.

As a result, general (overarching) state-of-the-art reference frameworks have been prepared for the associated degree, bachelor, master and doctorate, as well as aligned qualifications reference frameworks and more detailed learning outcomes/assessment frameworks for 11 subject areas, offering a sound basis for quality assurance. As a follow-up, actual assessment formats for five academic fields have been developed to allow for measuring the actual level of learning at the institutional level from a comparative perspective.

Frameworks as well as assessment models and items are highly innovative, content-wise as in the strategy of development, involving renown academics finding common ground. Its value is not limited to Europe but has global significance. The model developed, is also relevant for micro-credentials in defining levels of mastery.

The first-aid courses organized by the Youth Red Cross Carinthia (Austria) had a quality problem, necessitating a professionalization in teaching and time structure. This research aimed to enhance the quality and effectiveness of these courses by implementing modified lesson studies with non-professional trainers. The paper presents the realization process, empirical research and results obtained by applying the first-aid curriculum.

Around 22 lesson study first-aid courses (14 classes with 2 cycles, 8 with 3 cycles) were conducted and evaluated in different Austrian school types. An observation sheet was created to evaluate attention and competencies. Interviews were conducted with both teachers and students to validate the results.

The research findings demonstrate that lesson studies can significantly enhance the quality and effectiveness of first-aid courses. Inexperienced and experienced first-aid teachers significantly improved their teaching skills. Newly educated first-aid teachers showed substantial improvement, leading to the introduction of an induction period and coaching opportunity within the Youth Red Cross Carinthia.

This is the first lesson study conducted in a non-academic context. It highlights the adaptation process of Carinthian first-aid courses. It illustrates how lesson studies impact lesson clarity, instructional variety, student engagement in the learning process, student outcome, student feedback and teaching effectiveness in a non-academic context. It contributes to the literature on the application of lesson study in first-aid education and provides insight into the benefits of this approach in enhancing the quality of first-aid training.

District-university partners increasingly rely on “grow-your-own” licensure programs to address teacher shortages. Because vacancies in special education represent a chronic issue, our district-university partnership developed LEAP – the Licensed Educators’ Accelerated Pathway, successfully preparing 26 paraprofessionals as special education teachers (SEs). We describe a model university-district partnership in which we collaborated to design and implement paraprofessionals’ SE licensure program.

In this general review, we describe a district-university partnership collaboration that resolved barriers experienced by paraprofessionals working toward licensure in special education (Essential #4, Reflection and Innovation). The specialized design and partnership solutions were grounded in SE preparation research literature.

25 (28 entered the program and 25 completed) paraprofessionals from one large urban and several regional districts completed special education licensure through LEAP. Slightly more than half of LEAP participants were Black or Hispanic (see Table 1), contributing to the diversification of SE workforce. University-district partnership was successful in designing and delivering a program that allowed participants: a) to remain employed, b) attend evening classes in their geographic region or online, c) complete all field experiences in sponsoring districts (Essential #2) and d) receive concierge advising from a “completion coach.” We describe solutions to barriers experienced by paraprofessionals and advocate for district-university collaboration to address chronic teacher shortages.

Limitations include lack of data on success of program completers during their first year of teaching as they began this work in Fall 2023. Further, because the participating district was large and urban, generalization of program details for small and rural districts is difficult.

Practical tips for developing grow-your-own special education licensure programs are providing. Detailed descriptions of barriers candidates experienced and ways the district-university partners resolved these issues are included. Programs like the one described has the potential to positively impact teacher pipeline issues.

The program described provided highly-trained teachers to fill chronic vacancies in special education in three participating districts/agencies. Because students receiving special education services are at risk for school failure and are disproportionately impacted by teacher turnover, addressing this area through grow-your-own licensure programs represents a diversity, equity and inclusion initiative. Further, upskilling diverse paraprofessionals to licensed teacher roles represent an economic boost, which they might not otherwise have achieved.

Available research literature signals alarm over persistent teacher shortages in hard-to-staff districts and lack of diversity in the teacher workforce, but few published accounts describe successful programs. Partner collaboration fostered a re-imagining of course formatting and delivery to accommodate adult learners, avoiding problems often reported with alternative programs.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

This study aims to understand how practitioners use their insights in software development models to share experiences within and between organizations.

This is a qualitative study of practitioners in software development projects, in large-, medium- or small-size businesses. It analyzes interview material in three-step iterations to understand reflexive practice when using software development models.

The study shows how work processes are based on team members’ experiences and common views. This study highlights the challenges of organizational learning in system development projects. Current practice is unreflective, habitual and lacks systematic ways to address recurring problems and share information within and between organizations. Learning is episodic and sporadic. Knowledge from previous experience is individual not organizational.

Software development teams and organizations tend to learn about, and adopt, software development models episodically. This research expands understanding of how organizational learning takes place within and between organizations with practitioners who participate in teams. Learnings show the potential for further research to determine how new curriculums might be formed for teaching software development model improvements.

This paper offers a definition of the core of information science, which encompasses most research in the field. The definition provides a unique identity for information science and positions it in the disciplinary universe.

After motivating the objective, a definition of the core and an explanation of its key aspects are provided. The definition is related to other definitions of information science before controversial discourse aspects are briefly addressed: discipline vs. field, science vs. humanities, library vs. information science and application vs. theory. Interdisciplinarity as an often-assumed foundation of information science is challenged.

Information science is concerned with how information is manifested across space and time. Information is manifested to facilitate and support the representation, access, documentation and preservation of ideas, activities, or practices, and to enable different types of interactions. Research and professional practice encompass the infrastructures – institutions and technology –and phenomena and practices around manifested information across space and time as its core contribution to the scholarly landscape. Information science collaborates with other disciplines to work on complex information problems that need multi- and interdisciplinary approaches to address them.

The paper argues that new information problems may change the core of the field, but throughout its existence, the discipline has remained quite stable in its central focus, yet proved to be highly adaptive to the tremendous changes in the forms, practices, institutions and technologies around and for manifested information.