Analysing supply chain operation dynamics through logic-based modelling and simulation

Supply Chain Management (SCM) is becoming increasingly important in the modern business world. In order to effectively manage and integrate a supply chain (SC), a deep understanding of overall SC operation dynamics is needed. This involves understanding how the decisions, actions and interactions between SC members affect each other, and how these relate to SC performance and SC disruptions. Achieving such an understanding is not an easy task, given the complex and dynamic nature of supply chains. Existing simulation approaches do not provide an explanation of simulation results, while related work on SC disruption analysis studies SC disruptions separately from SC operation and performance. This thesis presents a logic-based approach for modelling, simulating and explaining SC operation that fills these gaps. SC members are modelled as logicbased intelligent agents consisting of a reasoning layer, represented through business rules, a process layer, represented through business processes and a communication layer, represented through communicative actions. The SC operation model is declaratively formalised, and a rule-based specification is provided for the execution semantics of the formal model, thus driving the simulation of SC operation. The choice of a logic-based approach enables the automated generation of explanations about simulated behaviours. SC disruptions are included in the SC operation model, and a causal model is defined, capturing relationships between different types of SC disruptions and low SC performance. This way, explanations can be generated on causal relationships between occurred SC disruptions and low SC performance. This approach was analytically and empirically evaluated with the participation of SCM and business experts. The results indicate the following: Firstly, the approach is useful, as it allows for higher efficiency, correctness and certainty about explanations of SC operation compared to the case of no automated explanation support. Secondly, it improves the understanding of the domain for non-SCM experts with respect to their correctness and efficiency; the correctness improvement is significantly higher compared to the case of no prior explanation system use, without loss of efficiency. Thirdly, the logic-based approach allows for maintainability and reusability with respect to the specification of SC operation input models, the developed simulation system and the developed explanation system

Discovering students’ learning strategies in a visual programming MOOC through process mining techniques

Funding: This work was supported by the Medical Research Council [grant number MR/N013166/1].Understanding students’ learning patterns is key for supporting their learning experience and improving course design. However, this is particularly challenging in courses with large cohorts, which might contain diverse students that exhibit a wide range of behaviours. In this study, we employed a previously developed method, which considers process flow, sequence, and frequency of learning actions, for detecting students’ learning tactics and strategies. With the aim of demonstrating its applicability to a new learning context, we applied the method to a large-scale online visual programming course. Four low-level learning tactics were identified, ranging from project- and video-focused to explorative. Our results also indicate that some students employed all four tactics, some used course assessments to strategize about how to study, while others selected only two or three of all learning tactics. This research demonstrates the applicability and usefulness of process mining for discovering meaningful and distinguishable learning strategies in large courses with thousands of learners.Publisher PD

Providing insights into health data science education through artificial intelligence

We would like to thank the Precision Medicine programme of the University of Edinburgh, as well as the Medical Research Council, for their support of this project aimed at enhancing health data science education. Additionally, we would like to express our appreciation to the Coursera platform and the students who participated in the course, whose contribution was invaluable to this research. This work was supported by the Medical Research Council [grant number MR/N013166/1].Background: Health Data Science (HDS) is a novel interdisciplinary field that integrates biological, clinical, and computational sciences with the aim of analysing clinical and biological data through the utilisation of computational methods. Training healthcare specialists who are knowledgeable in both health and data sciences is highly required, important, and challenging. Therefore, it is essential to analyse students’ learning experiences through artificial intelligence techniques in order to provide both teachers and learners with insights about effective learning strategies and to improve existing HDS course designs. Methods: We applied artificial intelligence methods to uncover learning tactics and strategies employed by students in an HDS massive open online course with over 3,000 students enrolled. We also used statistical tests to explore students’ engagement with different resources (such as reading materials and lecture videos) and their level of engagement with various HDS topics. Results: We found that students in HDS employed four learning tactics, such as actively connecting new information to their prior knowledge, taking assessments and practising programming to evaluate their understanding, collaborating with their classmates, and repeating information to memorise. Based on the employed tactics, we also found three types of learning strategies, including low engagement (Surface learners), moderate engagement (Strategic learners), and high engagement (Deep learners), which are in line with well-known educational theories. The results indicate that successful students allocate more time to practical topics, such as projects and discussions, make connections among concepts, and employ peer learning. Conclusions: We applied artificial intelligence techniques to provide new insights into HDS education. Based on the findings, we provide pedagogical suggestions not only for course designers but also for teachers and learners that have the potential to improve the learning experience of HDS students.Peer reviewe

Early prediction of student performance in a health data science MOOC

Funding: This work was supported by the Medical Research Council [grant number MR/N013166/1].Massive Open Online Courses (MOOCs) make high-quality learning accessible to students from all over the world. On the other hand, they are known to exhibit low student performance and high dropout rates. Early prediction of student performance in MOOCs can help teachers intervene in time in order to improve learners' future performance. This is particularly important in healthcare courses, given the acute shortages of healthcare staff and the urgent need to train data-literate experts in the healthcare field. In this paper, we analysed a health data science MOOC taken by over 3,000 students. We developed a novel three-step pipeline to predict student performance in the early stages of the course. In the first step, we inferred the transitions between students' low-level actions from their clickstream interactions. In the second step, the transitions were fed into Artificial Neural Network (ANN) that predicted student performance. In the final step, we used two explanation methods to interpret the ANN result. Using this approach, we were able to predict learners' final performance in the course with an AUC ranging from 83% to 91%. We found that students who interacted predominately with lab, project, and discussion materials outperformed students who interacted predominately with lectures and quizzes. We used the DiCE counterfactual method to automatically suggest simple changes to the learning behaviour of low- and moderate-performance students in the course that could potentially improve their performance. Our method can be used by instructors to help identify and support struggling students during the course.Publisher PD