Framework to Enhance Teaching and Learning in System Analysis and Unified Modelling Language

Cowling, MA ORCiD: 0000-0003-1444-1563; Munoz Carpio, JC ORCiD: 0000-0003-0251-5510Systems Analysis modelling is considered foundational for Information and Communication Technology (ICT) students, with introductory and advanced units included in nearly all ICT and computer science degrees. Yet despite this, novice systems analysts (learners) find modelling and systems thinking quite difficult to learn and master. This makes the process of teaching the fundamentals frustrating and time intensive. This paper will discuss the foundational problems that learners face when learning Systems Analysis modelling. Through a systematic literature review, a framework will be proposed based on the key problems that novice learners experience. In this proposed framework, a sequence of activities has been developed to facilitate understanding of the requirements, solutions and incremental modelling. An example is provided illustrating how the framework could be used to incorporate visualization and gaming elements into a Systems Analysis classroom; therefore, improving motivation and learning. Through this work, a greater understanding of the approach to teaching modelling within the computer science classroom will be provided, as well as a framework to guide future teaching activities

Using moodle analytics for continuous e-assessment in a financial mathematics course at Polytechnic of Porto

The relevance of electronic learning, commonly called e-learning, has been growing exponentially in the last decade. Virtual learning environments (VLEs) disclosed new paths for interactions and motivation promotion, offering basic learning analytics functions and are becoming progressively popular. Moodle (acronym for Modular Object Oriented Dynamic Learning Environment) is one of the most used VLEs, it is a free learning management system distributed as Open Source. The VLE Moodle gives professors access to an “endless” use and performance database like the number of downloads for each resource, participation of students in courses, statistics of performed quizzes, among others. The data stored by Moodle offers a good and handy source for learning analytics. One popular definition, from the First International Conference on Learning Analytics and Knowledge in 2011, states that “Learning Analytics is the measurement, collection, analysis and reporting of data about students and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs”. Thus, using appropriate learning analytics methods and techniques, it would be helpful to analyze what particular learning activities or tools were practically used by students in Moodle, and to what extent. Considering the importance of the student engagement and the benefits of continuous assessment in higher education, as well as the impact of information and communications technology (ICT) on educational processes, it is important to integrate technology into continuous assessment practices. Since student engagement is connected to the quality of the student experience, increasing it is one way of enhancing quality in a higher education institution. In this study, will be demonstrated how the use of several educational resources and a low-stakes continuous weekly e-assessment in Moodle had a positive influence on student engagement in a second year undergraduate Financial Mathematics Course. Students felt that their increased engagement and improved learning was a straight result of this method. Furthermore, this suggests that wisely planned assignments and assessments can be used to increase student engagement and learning, and, as a result, contribute to improving the quality of student experience and success.info:eu-repo/semantics/publishedVersio

Modify Flipped Model of Co-regulation and Shared-regulation Impact in Higher Education, and Role of Facilitator on Student's Achievement

2021 International Conference on Computational Science and Computational Intelligence (CSCI'21)Flipped learning approach is a well-organized learning model leading to efficient active learning, effective peer-to-peer collaboration, and student-teacher interaction. However, to date, strategic implementation of co-regulation and shared regulation is rare in a flipped model in higher education. Hence, applying the self-regulation, co-regulation and shared-regulation strategies in flipped learning model is something necessary. Aims: This research is to propose and modify the current flipped learning model by adding some elements like providing some co-regulation and shared regulation strategies to enhance the level of student’s self-regulation skills giving rise to better student’s academic achievements by using technology next to instructor support. Methodology: The proposed model consists of the self-regulation, co-regulation and shared regulation strategies to enhance student’s academic performance in a peer-to-peer interactive way by creating a pool of scripted dialogical regulation questions to collaboratively assessment of the student’s self-regulation resulted from learning analytics and semantic analysis of the regulation dialogical questions and answers exchanged by students online. Results: The expected outcome of this research is a modified flipped model for students in higher education to strategically have an effective self-regulation and peer-to-peer co-regulation. Their enhancement leads to effective peer-to-peer collaboration and better academic success.N/

Utilizing Online Activity Data to Improve Face-to-Face Collaborative Learning in Technology-Enhanced Learning Environments

학위논문 (박사)-- 서울대학교 대학원 : 융합과학기술대학원 융합과학부(디지털정보융합전공), 2019. 2. Rhee, Wonjong .We live in a flood of information and face more and more complex problems that are difficult to be solved by a single individual. Collaboration with others is necessary to solve these problems. In educational practice, this leads to more attention on collaborative learning. Collaborative learning is a problem-solving process where students learn and work together with other peers to accomplish shared tasks. Through this group-based learning, students can develop collaborative problem-solving skills and improve the core competencies such as communication skills. However, there are many issues for collaborative learning to succeed, especially in a face-to-face learning environment. For example, group formation, the first step to design successful collaborative learning, requires a lot of time and effort. In addition, it is difficult for a small number of instructors to manage a large number of student groups when trying to monitor and support their learning process. These issues can amount hindrance to the effectiveness of face-to-face collaborative learning. The purpose of this dissertation is to enhance the effectiveness of face-to-face collaborative learning with online activity data. First, online activity data is explored to find whether it can capture relevant student characteristics for group formation. If meaningful characteristics can be captured from the data, the entire group formation process can be performed more efficiently because the task can be automated. Second, learning analytics dashboards are implemented to provide adaptive support during a class. The dashboards system would monitor each group's collaboration status by utilizing online activity data that is collected during class in real-time, and provide adaptive feedback according to the status. Lastly, a predictive model is built to detect at-risk groups by utilizing the online activity data. The model is trained based on various features that represent important learning behaviors of a collaboration group. The results reveal that online activity data can be utilized to address some of the issues we have in face-to-face collaborative learning. Student characteristics captured from the online activity data determined important group characteristics that significantly influenced group achievement. This indicates that student groups can be formed efficiently by utilizing the online activity data. In addition, the adaptive support provided by learning analytics dashboards significantly improved group process as well as achievement. Because the data allowed the dashboards system to monitor current learning status, appropriate feedback could be provided accordingly. This led to an improvement of both learning process and outcome. Finally, the predictive model could detect at-risk groups with high accuracy during the class. The random forest algorithm revealed important learning behaviors of a collaboration group that instructors should pay more attention to. The findings indicate that the online activity data can be utilized to address practical issues of face-to-face collaborative learning and to improve the group-based learning where the data is available. Based on the investigation results, this dissertation makes contributions to learning analytics research and face-to-face collaborative learning in technology-enhanced learning environments. First, it can provide a concrete case study and a guide for future research that may take a learning analytics approach and utilize student activity data. Second, it adds a research endeavor to address challenges in face-to-face collaborative learning, which can lead to substantial enhancement of learning in educational practice. Third, it suggests interdisciplinary problem-solving approaches that can be applied to the real classroom context where online activity data is increasingly available with advanced technologies.Abstract i Chapter 1. Introduction １ 1.1. Motivation １ 1.2. Research questions ４ 1.3. Organization ６ Chapter 2. Background ８ 2.1. Learning analytics ８ 2.2. Collaborative learning ２２ 2.3. Technology-enhanced learning environment ２７ Chapter 3. Heterogeneous group formation with online activity data ３５ 3.1. Student characteristics for heterogeneous group formation ３６ 3.2. Method ４１ 3.3. Results ５１ 3.4. Discussion ５９ 3.5. Summary ６４ Chapter 4. Real-time dashboard for adaptive feedback in face-to-face CSCL ６７ 4.1. Theoretical background ７０ 4.2. Dashboard characteristics ８１ 4.3. Evaluation of the dashboard ９４ 4.4. Discussion １０７ 4.5. Summary １１４ Chapter 5. Real-time detection of at-risk groups in face-to-face CSCL １１８ 5.1. Important learning behaviors of group in collaborative argumentation １１８ 5.2. Method １２０ 5.3. Model performance and influential features １２５ 5.4. Discussion １２９ 5.5. Summary １３２ Chapter 6. Conclusion １３４ Bibliography １４０Docto

Analysing the learning pathways of students in a large flipped engineering course

Recent advancements in educational technologies (learning management systems, online discussion forums, peer-learning tools) coupled with new methods of course delivery (e.g. blended, flipped, MOOCs) provide significant opportunities for universities to deliver challenging, high quality, yet engaging curriculum for students. In this paper, we examine the variations and similarities of student's approaches to learning (learning pathways) by examining how well they performed in a large (N ~ 1000 student) first year engineering flipped classroom. The analysis focused on student's performance in their assessment (formative and summative) as well as their online interaction with a range of tools purposely built to support students through peer learning and acquisition of resources and expertise. Analysis using k-means clustering reveals that students do in fact adopt a variety of successful pathways through the course. The unique aspects of this work lie in the use of analytics algorithms that whilst perhaps routinely utilised in data mining, are not as well utilised in better understanding patterns (successful or otherwise) of student interactions within a technology enhanced active learning environment that integrates theory with engineering practice

Enhancing education and training through data-driven adaptable games in flipped classrooms

The Flipped Classroom (FC) is a set of pedagogical approaches that move the information transmission out of class and exploit class time for active and/or peer learning activities. In this context, students are required to engage with pre- and/or post-class activities in order to prepare themselves for class work. The FC instruction method has already been used in conjunction with other learning strategies. This theoretical paper presents the first developmental steps of a research project, which aims at building the FC through a fully bespoke and personalized experience, by using data-driven adaptable games and problem-based learning elements to improve the learning experience. The project will develop a gaming platform that will support the whole FC in a cyclical perspective, and aims to use the resources of gamification in a more significant manner that could go beyond score tracking and badges. Moreover, the problem-based learning approach will be used to better frame the learning activities included in FCs, while learning analytics features will provide adaptable learning pathways. The potential of this approach is to build a better FC experience for all the stakeholders. Students will be given more agency to calibrate their learning experience, while educators can monitor the students’ progress more effectively and adjust their learning activities accordingly. Finally, researchers will get better insight into the FC learning process, and the mechanics, which contribute to optimize the learning experience

A conceptual analytics model for an outcome-driven quality management framework as part of professional healthcare education

BACKGROUND: Preparing the future health care professional workforce in a changing world is a significant undertaking. Educators and other decision makers look to evidence-based knowledge to improve quality of education. Analytics, the use of data to generate insights and support decisions, have been applied successfully across numerous application domains. Health care professional education is one area where great potential is yet to be realized. Previous research of Academic and Learning analytics has mainly focused on technical issues. The focus of this study relates to its practical implementation in the setting of health care education. OBJECTIVE: The aim of this study is to create a conceptual model for a deeper understanding of the synthesizing process, and transforming data into information to support educators’ decision making. METHODS: A deductive case study approach was applied to develop the conceptual model. RESULTS: The analytics loop works both in theory and in practice. The conceptual model encompasses the underlying data, the quality indicators, and decision support for educators. CONCLUSIONS: The model illustrates how a theory can be applied to a traditional data-driven analytics approach, and alongside the context- or need-driven analytics approach

A Game Based Learning Design for Virtual Flipped Classrooms

Game-based learning and the flipped classroom method are relatively new approaches of engaging university students and enhancing their learning experience. A review of the literature shows that there are important areas that need to be further examined including how students’ self-reflection process can be supported in game-based learning and/or flipped classroom environments and the circumstances, under which these approaches are effective. This developmental paper presents a learning design that uses game-based learning and learning analytics to support a flipped classroom that runs fully online. The learning design will be used for the delivery of a Degree Apprenticeship module, offered by a UK Business School. The aim is to understand a) whether such approaches can be followed successfully online and b) whether the learning design can meet the needs of Degree Apprenticeship students. The paper concludes with a brief discussion of the proposed methodology that will be followed to answer the above questions

Twelve tips for rapidly migrating to online learning during the COVID-19 pandemic

The COVID-19 pandemic has resulted in a massive adaptation in health professions education, with a shift from in-person learning activities to a sudden heavy reliance on internet-mediated education. Some health professions schools will have already had considerable educational technology and cultural infrastructure in place, making such a shift more of a different emphasis in provision. For others, this shift will have been a considerable dislocation for both educators and learners in the provision of education. To aid educators make this shift effectively, this 12 Tips article presents a compendium of key principles and practical recommendations that apply to the modalities that make up online learning. The emphasis is on design features that can be rapidly implemented and optimised for the current pandemic. Where applicable, we have pointed out how these short-term shifts can also be beneficial for the long-term integration of educational technology into the organisations' infrastructure. The need for adaptability on the part of educators and learners is an important over-arching theme. By demonstrating these core values of the health professions school in a time of crisis, the manner in which the shift to online learning is carried out sends its own important message to novice health professionals who are in the process of developing their professional identities as learners and as clinicians