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The authors offer useful information and guidelines for selecting, designing, and developing asynchronous and synchronous e-Learning courses that build knowledge and skills for workers learning in corporate, government, and academic settings.

The essential e-learning design manual, updated with the latest research, design principles, and examples e-Learning and the Science of Instruction is the ultimate handbook for evidence-based e-learning design. Since the first edition of this book, e-learning has grown to account for at least 40% of all training delivery media. However, digital courses often fail to reach their potential for learning effectiveness and efficiency. This guide provides research-based guidelines on how best to present content with text, graphics, and audio as well as the conditions under which those guidelines are most effective. This updated fourth edition describes the guidelines, psychology, and applications for ways to improve learning through personalization techniques, coherence, animations, and a new chapter on evidence-based game design. The chapter on the Cognitive Theory of Multimedia Learning introduces three forms of cognitive load which are revisited throughout each chapter as the psychological basis for chapter principles. A new chapter on engagement in learning lays the groundwork for in-depth reviews of how to leverage worked examples, practice, online collaboration, and learner control to optimize learning. The updated instructor's materials include a syllabus, assignments, storyboard projects, and test items that you can adapt to your own course schedule and students. Co-authored by the most productive instructional research scientist in the world, Dr. Richard E. Mayer, this book distills copious e-learning research into a practical manual for improving learning through optimal design and delivery. Get up to date on the latest e-learning research Adopt best practices for communicating information effectively Use evidence-based techniques to engage your learners Replace popular instructional ideas, such as learning styles with evidence-based guidelines Apply evidence-based design techniques to optimize learning games e-Learning continues to grow as an alternative or adjunct to the classroom, and correspondingly, has become a focus among researchers in learning-related fields. New findings from research laboratories can inform the design and development of e-learning. However, much of this research published in technical journals is inaccessible to those who actually design e-learning material. By collecting the latest evidence into a single volume and translating the theoretical into the practical, e-Learning and the Science of Instruction has become an essential resource for consumers and designers of multimedia learning.

BackgroundTeachers can have a significant impact on student interest and learning in science, technology, engineering, and math (STEM) subjects and careers. Teacher self-efficacy can also significantly affect student learning. Researchers investigated the effects of teacher professional development and integrated STEM curriculum development on teacher self-efficacy. Participants in the study included high school science and engineering technology teachers enrolled in a National Science Foundation–ITEST project called Teachers and Researchers Advancing Integrated Lessons in STEM (TRAILS). The TRAILS program sought to prepare teachers to integrate STEM content using engineering design, biomimicry, science inquiry, and 3D printing as pedagogical approaches. Teachers learned within a community of practice working alongside industry partners and college faculty. The purpose of the study was to investigate the impact of the 70 h of professional development to train three cohorts of teachers over 3 years on teacher self-efficacy. The research design utilized a quasi-experimental nonequivalent control group approach, including an experimental group and an untreated control group.ResultsMeasurements on beliefs about teacher self-efficacy were collected on pretest, posttest, and delayed posttest survey assessments. Researchers analyzed the T-STEM survey results for teaching self-efficacy using the Wilcoxson signed-rank test for detecting significant differences. Science teachers showed a significant increase in teacher self-efficacy comparing the pretest and delayed posttest scores after TRAILS professional development and STEM lesson implementation (p = .001, effect size = .95). Additionally, significant differences between groups (science experimental vs science control group teachers) using the Wilcoxon rank-sum test were detected from pretest to posttest (p = .033, effect size = .46), posttest to delayed posttest (p = .029, effect size = .47), and pretest to delayed posttest (p = .005, effect size = .64). There were no significant differences detected in the control group. Engineering technology teachers showed no significant differences between the pretest, posttest, and delayed posttest self-efficacy scores.ConclusionsThe results indicate the science teachers’ self-efficacy increased after professional development and after lesson implementation. Potential implications from this research suggest that the science teacher participants benefited greatly from learning within a community of practice, engaging in science practices, and using science knowledge to solve a real-world problem (engineering design).

This article reports the findings of a case study that investigated the perceptions of international students regarding the impact of cultural differences on their learning experiences in an online MBA program. The study also revealed that online instructors need to design courses in such a way as to remove potential cultural barriers, including language, communication tool use, plagiarism, time zone differences and a lack of multicultural content, which may affect international students' learning performances. The study indicates that a culturally inclusive learning environment needs to consider diversity in course design in order to ensure full participation by international students.

Contemporary industry practices must be appropriately reflected in designing modern-day teaching and learning programmes. Existing studies are limited to systematic methodologies for accumulating contemporary practice requirements and using that data to inform the design of educational programmes, even though various, local approaches for doing so often exist in higher education institutions. Going beyond this and adopting design-based research (DBR) principles, this paper introduces industry practitioner perspectives of contemporary- practice need for conceptualisation and design of a new business master’s degree programme. Outlining industry-demand as a driving force in stimulating a new business data-analytics programme at a medium-sized Australian metropolitan university, the study utilises open-ended interviews with five senior data analytics professionals to find a new matrix of industry expectations. The emerged elements are open-sourced tools based general technical knowledge; specific industry certifications or special skills; technology integration knowledge; cross-industry knowledge such as marketing; project management/agility and decision-making utilising appropriate supporting knowledge. Based on these findings, key learning objectives, an initial structure of the programme and specialisation subjects is proposed for further evaluation through convergent interviewing. We anticipated that the entire design process could be reusable for other similar situations for designing new practical courses in higher education sector.

Background Good communication skills among community health workers (CHWs) can improve public vaccination decision-making and alleviate vaccine hesitancy. However, in China, standardized training to improve the vaccine communication skills of CHWs is insufficient and underemphasized. Accordingly, this study examined the training preferences for CHWs’ vaccine communication skills to meet the current needs of community-based vaccination services in China. Methods This study employed a discrete choice experiment (DCE) design conducted at five community health service centers in Harbin, China, between April and June 2021. Data collection included in-depth interviews and face-to-face administration of questionnaires. CHWs were recruited via convenience sampling, with eligibility criteria requiring participants to: hold vaccination qualification certificates; be full-time frontline staff with ≥ 1 year of vaccination experience; have no prior formal training in vaccine communication. A total of 164 valid questionnaires were included for analysis. Preferences for training programs were evaluated using a mixed logit model. The DCE attributes comprised training period (1 day, 3 days, 7 days), main content (vaccine basics, addressing common misconceptions, communication skills), delivery methods (online lectures, on-site seminars, case simulations), training scale (< 50, 50–100, > 100 participants), and feedback mechanisms (none, final feedback, stage-based feedback). Results Three attributes—training period, main content of the training, and training scale—significantly influenced CHWs’ training choices. CHWs preferred programs with shorter training cycles and fewer than 50 participants to improve their ability to share basic vaccine-related knowledge and had less desire to receive communication skills training. At the same time, we found no association of training modality on CHW preferences. Conclusions Using a DCE, this study investigated the preferences of CHWs for participating in a vaccine communication training program. CHWs preferred a one-day training cycle, training content focusing on vaccine basics, and a small number of people to be trained. The results of this study can inform policymakers and administrators when in China developing vaccine communication training programs for CHWs.

PurposeThe purpose of this practitioner paper is to explore whether the principles of Design Thinking and the Lean Startup could be employed in developing a disruptive model for delivering educational programs within higher education in a way that attempts to eliminate the multitude of problems facing this industry, while simultaneously adhering to the principles of frugal innovation and meeting relevant sustainability goals.Design/methodology/approachThe authors followed a design thinking approach, employing tools such as empathy mapping, customer journey, value proposition and semi-structured interviews to obtain a deep level of understanding of the problems educators and students within the context of entrepreneurship education are facing. Throughout the process they drew on the practice of emergent inquiry and customer co-creation to help guide decision making.FindingsThe authors successfully derived a conceptual solution in the form of a Minimum Viable Product of which the features were tested against the multitude of user needs and requirements. It was possible to demonstrate how the solution meets all nine of the requirements for frugal innovations while simultaneously adhering to applicable sustainability principles.Practical implicationsThe proposed solution offers a potential opportunity to first-movers in chosen academic disciplines to become leaders in online education.Originality/valueEven in an industry such as higher education there is a dire need for frugality and finding sustainable solutions for educators and students in both developed and developing markets. With this paper the authors succeed in presenting innovative combinations of digital artefacts, platforms and infrastructure to arrive at a novel crowd-sourced solution that is unique in its design.

Here is this week’s news of grants or gifts to historically Black colleges and universities or for programs of particular interest to African Americans in higher education.

Children with autism spectrum disorder (ASD) usually show little interest in academic activities and may display disruptive behavior when presented with assignments. Research indicates that incorporating motivational variables during interventions results in improvements in behavior and academic performance. However, the impact of such motivational variables varies between children. In this paper, we aim to address the problem of selecting the right motivator for children with ASD using reinforcement learning by adapting to the most influential factors impacting the effectiveness of the contingent motivator used. We model the task of selecting a motivator as a Markov decision process problem. The states, actions and rewards design consider the factors that impact the effectiveness of a motivator based on applied behavior analysis as well as learners’ individual preferences. We use a Q-learning algorithm to solve the modeled problem. Our proposed solution is then implemented as a mobile application developed for special education plans coordination. To evaluate the motivator selection feature, we conduct a study involving a group of teachers and therapists and assess how the added feature aids the participants in their decision-making process of selecting a motivator. Preliminary results indicated that the motivator selection feature improved the usability of the mobile app. Analysis of the algorithm performance showed promising results and indicated improvement of the recommendations over time.

This article examines the effects of integrating ChatGPT, a generative language model developed by OpenAI, into educational and training contexts in higher education. The research takes as its conceptual framework models of technology acceptance and questions the relevance of these models to the acceptance and adoption of ChatGPT. A qualitative study carried out with teachers from various higher education establishments in France enables us to propose a model adapted to the specific features of generative AI. The ethical dimension and the controllability of the tools by users, made possible by a progressive training program, are two constructions that are essential to a proper understanding of whether or not these new tools are adopted. Additionally, this research contributes to the growing discourse on how generative AI innovations can be leveraged to enhance digital transformation in the academic sector, with a particular focus on business schools' stakeholders and strategies. Finally, the contributions and prospects for future research are discussed.