42 research outputs found
Designing blended learning courses: exploring the development and use of a toolkit to facilitate design
No full textIt is becoming increasingly evident that blended learning can overcome various limitations related to face-to-face instruction and online learning. Many studies have found that blended learning is becoming the norm for course delivery in higher education, with students identifying blended courses as best supporting how they learn. The question, therefore, has shifted from “Whether to blend or not?” to “how to design a successful blend?”. The literature shows that designing blended learning courses is still a major challenge for many academics in the higher education field. To enhance the understanding of blended learning course design and contribute towards the developing of the existing literature in this area, this research investigated the design of blended learning courses. It focused on two major design challenges: (i) deciding the proportion of online to face-to-face components to be incorporated into a blended course; and (ii) selecting the most appropriate delivery methods to achieve the course outcomes. A Delphi study of two rounds was conducted to identify and rate the importance of criteria that might influence each design challenge. Another Delphi study of two rounds was also conducted to assess the impacts of the identified criteria on the design of blended courses. The results of the two Delphi studies have informed the development of a blended learning design toolkit that can assist academics in designing their courses. The toolkit was developed as an online tool using C# and ASP.NET. The toolkit was also evaluated using a functional testing, a performance testing and an evaluation survey with 12 experts. The evaluation results demonstrated that the toolkit provides a clear, simple and efficient blended learning design process that can foster thinking about how to better design a blended course
Building student support for computing students:How do students respond to different models?
No full textOver the last two years in the School of Informatics at the University of Edinburgh, we have implemented a new approach to student support for our undergraduate and masters students, integrating new approaches to practical support and well-being with a range of co- and extra-curricula events, designed to help computing students develop more completely as future employees and citizens. In this paper, we outline the new approach, comparing it with our traditional approach to student support in our department, and consider how successful this switch has been through interviews with twenty-six students. Our research indicates that the key things that students value in student support are reliability and consistency, and that whilst engaging computing students in non-core activities is challenging, there are approaches that can help - in particular, being very specific how students will benefit through attending and allowing flexibility in routes to engagement
Data systems education : curriculum recommendations, course syllabi, and industry needs
No full textData systems have been an important part of computing curricula for decades, and an integral part of data-focused industry roles such as software developers, data engineers, and data scientists. However, the field of data systems encompasses a large number of topics ranging from data manipulation and database distribution to creating data pipelines and data analytics solutions. Due to the slow nature of curriculum development, it remains unclear (i) which data systems topics are recommended across diverse higher education curriculum guidelines, (ii) which topics are taught in higher education data systems courses, and (iii) which data systems topics are actually valued in data-focused industry roles. In this study, we analyzed computing curriculum guidelines, course contents, and industry needs regarding data systems to uncover discrepancies between them. Our results show, for example, that topics such as data visualization, data warehousing, and semi-structured data models are valued in industry, yet seldom taught in courses. This work allows professionals to further align curriculum guidelines, higher education, and data systems industry to better prepare students for their working life by focusing on relevant skills in data systems education
Curriculum analysis for data systems education.
No full textThe field of data systems has seen quick advances due to the popularization of data science, machine learning, and real-time analytics. In industry contexts, system features such as recommendation systems, chatbots and reverse image search require efficient infrastructure and data management solutions. Due to recent advances, it remains unclear (i) which topics are recommended to be included in data systems studies in higher education, (ii) which topics are a part of data systems courses and how they are taught, and (iii) which data-related skills are valued for roles such as software developers, data engineers, and data scientists. This working group aims to answer these points to explain the state of data systems education today and to uncover knowledge gaps and possible discrepancies between recommendations, course implementations, and industry needs. We expect the results to be applicable in tailoring various data systems courses to better cater to the needs of industry, and for teachers to share best practices
A plan for a joint study into the impacts of AI on professional competencies of IT professionals and implications for computing students.
No full textAs Artificial Intelligence (AI) continues to make its presence felt in transforming workplaces around the world, and the Information Technology industry in particular, it is essential to understand its impact on the work practices of IT professionals, and the implications for computing students and curricula. This research project builds on work initiated jointly, in Sweden, New Zealand and Scotland, investigating concerns about the increasing impacts of Artificial Intelligence in IT Sector workplaces for employee work engagement and the implications for tertiary study, assessment and curricula in computing. "Work engagement", has been defined as the positive inner state where employees are fully present and engaged in their work, and is closely linked to motivation, learning, productivity, and accountability. Within the context of (Generative) AI at work, IT professionals have been noted as early adopters of AI. Their involvement in implementing and utilising AI technologies can provide valuable insights into the interplay between AI and work engagement. The implications for students are significant as future IT professionals, who must acquire and enhance competencies to adapt and thrive in digital workplaces
Developing a Model of First Year Student Satisfaction in a Studio-based Teaching Environment
No full textAll for One and One for All - Collaboration in Computing Education: Policy, Practice, and Professional Dispositions
No full textThe ITiCSE’23 final keynote raised teaching soft skills, or professional dispositions, to help students face challenges in modern programming. This project addresses helping computing students develop professional dispositions through collaborative learning (CL) since some in the industry observe entry-level engineers struggling due to their fragile professional dispositions. We are motivated to understand professional expectations from entry-level engineers and present the academia-industry gap to support practitioners and researchers in advancing CL in Computing Education, encouraging positive curricula and policy changes that promote DEIA. We will present CL practices alongside their supported professional dispositions to assist practitioners in adoption. We will present the academia-industry gap in CL for future research opportunities, helping researchers advance CL practices to integrate professional dispositions the industry expects from entry-level engineers.</p
