OEGlobal 2024

Open educational resources are a well-established model for universities, but high school students often struggle to use these resources effectively. In Taiwan, the updated Curriculum Guidelines require high school students to engage in self-directed learning each semester. This aims to help them explore their academic interests and identities before university. When applying to universities, students’ learning portfolios, which highlight their interests and academic potentials, are crucial for admission. Therefore, universities must provide high-quality open classrooms accessible to high school students. These courses can help students develop academic interests, mindsets, and self-directed learning capabilities.

This approach not only prepares students for future academic success but also promotes a culture of openness, sharing, and collaboration. Leveraging university open classrooms for high school students benefits both the students and the broader educational community. In this presentation, we introduce a case study of establishing an open classroom using a university's general education course, Contemporary Cognitive Neuroscience: Brain and Mind.

By exploring community of inquiry and learning engagement theories, this study establishes a five-step model to transform a large class into a highly interactive online format. By integrating livestreaming technology and platforms like YouTube live streaming, Slido classroom interaction software, and social media such as Facebook and Instagram, the five steps are: immersive live lectures, real-time polling and quick Q&A, filtering crowdsourced questions, extending learning through summaries and reflections, and knowledge sharing on social media. This study employs design-based research with 768 students participating.

Through pre- and post-tests, surveys, platform data, and qualitative research data, the results show:

1. students' academic performance significantly improved, with high school students outperforming university students in the post-test;
2. the new learning model showed significant improvements in students' agentic, behavioral, emotional, and cognitive engagement, as well as critical thinking, with no significant difference in social engagement;
3. nearly 40% of students completed the final project through team collaboration using online tools like Instagram chat, Google Meet, and Google Slides;
4. students initially felt shy and awkward but gradually enjoyed and felt accomplished in knowledge sharing;
5. students used digital note-taking, integrating screenshots, typed notes, and handwriting.

Creating an open classroom for high school students is exciting and rewarding, but it requires significant effort, including human and economic resources. Universities aiming to promote open education should formulate regulations, policies, or funding grants to support teaching teams in creating open classrooms. This project's open large-class interactive teaching method can serve as a reference for universities in promoting open classrooms and conducting highly interactive teaching in the future. Establishing a robust support system can ensure sustainability and continuous improvement in delivering open educational resources to a broader audience. This holistic approach will enhance the learning experience for high school students and contribute to the overall advancement of the educational landscape.



Included in [Session 10C]: Practice in OE

Author Keywords
Open educational practices, Digital competence, Sustainability, Open Classroom, Learning Engagement, Live-Streaming Technology

Over the past decade, the Taiwan Ministry of Education (MoE) has launched a series of e-learning initiatives to improve educational quality and accessibility. Since 2014, the MoE has funded multiple 3-year projects, each focusing on different aspects of e-learning development.

The inaugural project, initiated in 2014, was a transformative step towards modernizing Taiwanese education. It encouraged educators to overhaul their teaching methods, utilizing digital tools to create high-quality online courses tailored to specific subject areas. This shift towards a more dynamic and interactive learning environment marked a departure from traditional classroom settings, accommodating diverse learning styles.

Following the success of the first project, the MoE launched a second three-year endeavor from 2017 to 2019. This phase aimed to deepen the integration of e-learning into higher education institutions. The focus shifted towards developing interconnected series of courses, enabling universities to offer micro-credit programs. A total of 66 course series were established during this phase, significantly expanding e-learning offerings across Taiwanese universities.

The third phase, starting in 2019, represented a strategic response to the evolving educational landscape, with an emphasis on fostering digital learning readiness. Participating universities were tasked with formulating comprehensive plans to promote e-learning among faculty and students, including the establishment of support teams and incentive structures.

Building on these initiatives, the MoE initiated a second round of funding from 2022 onwards to optimize online learning experiences and extend exemplary courses to neighboring Southeast Asian countries. This involved reconfiguring the project architecture to introduce an alliance-based model for university participation. Each alliance comprised a central hub university with extensive e-learning experience and several partner universities eager to learn from their expertise.

During the initial phase of this four-year project (2022-2023), six alliances involving 32 universities were formed, fostering collaboration within the Taiwanese e-learning ecosystem. As the project progressed, alliances and university compositions were restructured to better align with evolving priorities. By the latter half of the project (2024-2025), five alliances comprising 27 universities were actively engaged in advancing the e-learning agenda.

The current phase of the project focuses on empowering educators, guiding students, and fostering vibrant local ecosystems conducive to educational innovation. This includes developing strategies to incentivize instructional redesign and integrate emerging educational technologies such as AI tutors.

In conclusion, the MoE's e-learning initiatives have made significant strides in promoting online education within Taiwan and beyond. This presentation aims to highlight these achievements and inspire universities to continue developing high-quality online courses, positioning Taiwan as a leading source of e-learning excellence in the region.



Included in [Session 10C]: Practice in OE

Author Keywords
E-Learning, Project Movement, Alliance-based Model

Today’s higher education (HE) students often need to bridge knowledge and skills gaps for things that are not explicitly covered in their course curriculum. For example, students may need to create a presentation and record it as a video for an assessment, yet they are not taught how to do this. Flexible and timely self-paced options that leverage Technology Enhanced Learning (TEL) can help to address gaps such as these but need to cater for specific needs of time-poor students. This presentation outlines early research models and findings into the use of open microlearning, a form of microlearning that is based on the principles of open educational practices (OEP), for self-directed learning at Charles Darwin University. Open microlearning offers quick, bite-sized learning (usually 5-15 minutes) that leverage freely available and reusable materials, as well as collaboration with others, to meet specific learning needs. The research centres on the opportunities and benefits that open microlearning can offer, and models that can be used for design and implementation.

The research is informed by a comprehensive literature review, and data collected from staff and students at Charles Darwin University. The study utilises a Design-Based Research (DBR) methodology that provides an iterative and collaborative approach for designing, testing, and refining interventions in real-world educational settings (McKenney & Reeves, 2013). While microlearning and open practice are not new concepts, research reveals limited familiarity and use within HE contexts. The study highlights key elements for development models with open microlearning, including the importance of micro-assessment and reflection as part of open microlearning interventions.

Open microlearning can assist lecturers and learning designers to develop streamlined and engaging TEL materials for supplementary and extension activities to suit individual student needs in a wide variety of contexts. The focused, micro-format aligns with the trend in adult learner preferences for shorter, more informal educational activities (Bannister et al, 2020) and accommodates busy student schedules.

Open microlearning is a multifaceted construct that requires careful consideration to provide targeted learning to address specific knowledge or skill sets. Properly applied, open microlearning can facilitate effective and efficient learning with reduced cognitive load (Lee, 2021). Inclusion of OEP promotes access and equity in education through the sharing of high-quality resources and reduction of costs (Ossiannilsson, 2020). This study is part of a broader PhD research project around open microlearning as self-directed learning in higher education. In this research open microlearning is not aimed at replacing traditional accredited training, but rather is used for self-directed learning to address knowledge gaps and contribute to improved student success.



Included in [Session 10C]: Practice in OE

References
Bannister, J., Neve, M., & Kolanko, C. (2020). Increased Educational Reach through a Microlearning Approach: Can Higher Participation Translate to Improved Outcomes? Journal of European CME, 9(1), 1834761-1834761. https://doi.org/10.1080/21614083.2020.1834761 Lee, Y.-M. (2021). Mobile microlearning: a systematic literature review and its implications. Interactive learning environments, 1-16. https://doi.org/10.1080/10494820.2021.1977964 McKenney, S. E., & Reeves, T. C. (2013). Systematic Review of Design-Based Research Progress: Is a Little Knowledge a Dangerous Thing? Educational researcher, 42(2), 97-100. https://doi.org/10.3102/0013189X12463781 Ossiannilsson, E. (2020). Quality models for open, flexible, and online learning. Journal of Computer Science Research.

Author Keywords
Open educational Practice, Microlearning, Self-directed learning, open microlearning

The burgeoning integration of Large Language Models (LLMs) such as ChatGPT into the fabric of Massive Open Online Courses (MOOCs) has highlighted a promising new direction for enhancing automated essay assessment processes. This research delves into the practical implementation of LLMs for evaluating student essays within MOOC frameworks, focusing primarily on exploring advanced prompt engineering strategies.

We investigate a spectrum of methodologies, including few-shot learning, Chain-of-Thought (CoT) prompting, and fine-tuning techniques, to discern the most effective strategies for harnessing the capabilities of LLMs in this educational domain. Drawing from the latest advancements in natural language processing (NLP), our study examines the ability of LLMs to deliver accurate, efficient, and scalable assessments of student submissions.

MOOCs typically host hundreds to thousands of students per course, presenting significant logistical challenges regarding assignment evaluation. The volume of essays that require assessment can be overwhelming for instructors, making it virtually impossible to provide detailed, timely feedback without technological assistance. The deployment of LLMs promises not only to enhance the grading efficiency and maintain consistency in evaluation standards across large cohorts.

The primary objective of this study is to explore the application of generative AI (GAI) in assisting with essay grading, utilizing open courses hosted at ewant, the largest MOOCs platform run by National Yang Ming Chiao Tung University (NYCU) in Taiwan . This course, "Required Credits for University Students - Emotional Education" is taught by Professor Chen Fei-Chuan at National Yunlin University of Science and Technology, Taiwan. Since its first delivery in 2015, this course has been offered 137 times, with nearly 20,000 students enrolled. From both qualitative and quantitative perspectives, this course represents an optimal choice for the study, offering substantial potential for further research and development. Assignments in this course predominantly involve open-ended questions without standard answers, encouraging students to reflect, discuss, share, and synthesize their personal experiences based on the knowledge acquired during the course. This type of unstructured assignment is better suited for introducing GAI than structured assignments in science and engineering courses with definitive answers.

This research aims to leverage a data-driven approach to develop a GAI system that replicates the grading standards and performance of the instructors or teaching assistants (graders), thereby assisting future educators in efficiently grading large volumes of written assignments. By analyzing the strengths and drawbacks of multiple prompt engineering and fine-tuning methods in automating essay evaluations, the study aims to establish a dataflow pipeline for AI-assisted essay grading, with the expectation of generalizing this process to other courses of a similar nature. Additionally, this research proposes recommendations for designing more effective and scalable automated essay assessment systems tailored for contemporary online education platforms.

Overall, this study aims to provide a comprehensive analysis of the potential of LLMs in transforming the landscape of essay assessment in MOOCs, thereby contributing valuable insights into the optimization of educational technologies in a GAI era.



Included in [Session 11A]: Artificial Intelligence

References
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877-1901.

Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022). Large language models are zero-shot reasoners. Advances in neural information processing systems, 35, 22199-22213.

Min, S., Lyu, X., Holtzman, A., Artetxe, M., Lewis, M., Hajishirzi, H., & Zettlemoyer, L. (2022). Rethinking the role of demonstrations: What makes in-context learning work?. arXiv preprint arXiv:2202.12837.

Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., ... & Le, Q. V. (2021). Finetuned language models are zero-shot learners. arXiv preprint arXiv:2109.01652.

Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., ... & Zhou, D. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35, 24824-24837.

Author Keywords
Artificial Intelligence, Large Language Models, Prompt Engineering, Assessment, MOOCs