A previous study on the use of the Empath library in the prediction of Service Level Agreements (SLA) reveals the quality levels required for meaningful interaction between government customer service officers and citizens. On the other hand, past implementation of the Citizen Question-Answer system (CQAS), a type of Question-Answer model, suggests that such models if put in place can empower government customer service officers to reply faster and better with recommended answers. This study builds upon the research outcomes from both arenas of studies and introduces an innovative system design that allows the officers to incorporate the outputs from Empath X SLA predictor and CQAS (a type of Question Answer model) as critical inputs to ChatGPT engine, known as AI Based Citizen Question-Answer Recommender (ACQAR).Empath X SLA predictor anticipates the expected service response time based on citizens’ emotional state. These valuable inputs coupled with the recommended answer provided by the CQAS will serve as prompt inputs to ChatGPT to craft contextually aware responses. This ensures that the final response considers the citizen’s emotional needs, expected service timeline, and recommended answers from official government documents.While the full-scale deployment of this pilot system, ACQAR, is pending, this paper presents a comprehensive blueprint for governments seeking to modernize citizen service delivery. By fusing sentiment analysis, SLA prediction, question-answer models, and ChatGPT, this system design aims to revolutionize government-citizen interactions, delivering more empathetic, efficient, and tailored responses, while not violating SLA.This paper serves as a foundational step towards the practical development and implementation of an intelligent system (ACQAR) that holds the potential to significantly enhance citizen satisfaction, foster trust in government services, and strengthen overall government-citizen relationships.

This paper explores ChatGPT’s potential in aiding government agencies, drawing from a case study based on a government agency in Singapore. While ChatGPT’s text generation abilities offer promise, it brings inherent challenges, including data opacity, potential misinformation, and occasional errors. These issues are especially critical in government decision-making.Public administration’s core values of transparency and accountability magnify these concerns. Ensuring AI alignment with these principles is imperative, given the potential repercussions on policy outcomes and citizen trust.AI explainability plays a central role in ChatGPT’s adoption within government agencies. To address these concerns, we propose strategies like prompt engineering, data governance, and the adoption of interpretability tools such as SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). These tools aid in understanding and enhancing ChatGPT’s decision-making processes.This paper underscores the urgency for government agencies to adopt a proactive stance by proposing a 4-Steps framework completed with potential measures to enhance ChatGPT’s explainability within the specific context of public administration. Collaborative efforts between AI practitioners and public administrators are essential for striking an equilibrium between the capabilities of ChatGPT and the unique demands of government operations, ultimately ensuring a responsible integration of ChatGPT into public administration processes.

As computing projects increasingly become a core component of undergraduate courses, effective mentorship is crucial for supporting students' learning and development. Our study examines the adoption of ChatGPT as a mentor for undergraduate computing projects. It explores the impact of ChatGPT mentorship, specifically, skills development, and mentor responsiveness, i.e., ChatGPT’s responsiveness to students' needs and requests. We utilize PLS-SEM to investigate the interrelationships between different factors and develop a model that captures their contribution to the effectiveness of ChatGPT as a mentor. The findings suggest that mentor responsiveness and technical/design support are key factors for the adoption of AI tools like ChatGPT. The study provides practical implications for educators seeking to incorporate AI as a mentor to support students doing computing projects and contributes to the broader understanding of the use of AI in education.

Service Level Agreement (SLA) plays a significant role in the relationship between citizens and the government. It stipulates the quality levels required for the meaningful interaction between the two parties. Most SLA predictive models consider end-to-end duration and frequency of failed service requests as model inputs with little research on the analysis of textual details of the service request. This is an issue for government bodies as the latter do not just want to meet SLA, but also be proactive by knowing the citizens before assisting them. Inclusion of textual data potentially answer to this requirement of knowing the citizen before the officer tries to meet SLA. In this paper, we attempt to enrich SLA predictive process by analysing the textual data contained in the service requests. Based on a dataset of 800k case records from a customer service centre based in Singapore, we use text analytics to derive features from the dataset, which will be included with other commonly used variables in the prediction of SLA. We further explore the use of the Empath library to provide a categorical outcome that is more beneficial for the customer service officers to understand the citizen, than a numerical outcome. Based on our experiments, we observe that a predictive model built via logistic regression performs the best with an accuracy of 75%. This result remains valid when Empath categories are included as an input variable. This paper adds to the body of research work done in citizen service by proposing an SLA predictive model that incorporates lexical features from textual data to facilitate proactive citizen service delivery.

Today, computing graduates see a bright outlook thanks to the high demand for skills driven by the increasing importance of technology as the main driving force behind business transformation. As technology continues to grow exponentially over recent years, computing graduates have some of the highest starting salaries in the market [20] [19]. Even though universities have increased production of computing degree graduates in recent years, it is insufficient to fill the jobs available in the market [14]. Therefore, going forward, the demand is likely to further increase. The continued demand for computing programs in universities has led to an increased intake size, thus straining faculty workloads. Universities must consider funneling more resources into computing programs to address faculty’s increasing workload. Past studies have shown that peer learning environments enable increased productivity for faculty and enhanced educational quality for students [27] [15]. In this article, we describe a peer assisted learning model for an introductory programming course — Coders Assembly — designed and driven by undergraduate students in a computing program. Our model centers on four key areas – process, people, content, and technology. We reflect on our experience of implementing this peer assisted learning model and the result of a student survey.

Microservices-based applications consist of loosely coupled, independently deployable services that encapsulate units of functionality. To implement larger application processes, these microservices must communicate and collaborate. Typically, this follows one of two patterns: (1) choreography, in which communication is done via asynchronous message-passing; or (2) orchestration, in which a controller is used to synchronously manage the process flow. Choosing the right pattern requires the resolution of some trade-offs concerning coupling, chattiness, visibility, and design. To address this problem, we propose a decision framework for microservices collaboration patterns that helps solution architects to crystallize their goals, compare the key factors, and then choose a pattern using a weighted scoring mechanism. In cases where there is no clear preference, a hybrid pattern is suggested which inherits some strengths of both choreography and orchestration. We demonstrate the framework by evaluating the needs of three industry case studies (Danske Bank, LGB Bank, Netflix), showing that it leads to appropriate patterns being suggested. We are not aware of any existing decision frameworks to guide solution architects in choosing a microservices collaboration pattern.

Having students write short self-reflections at the end of each weekly session enables them to reflect on what they have learnt in the session and topics they find challenging. Analysing these self-reflections provides instructors with insights on how to address the missing conceptions and misconceptions of the students and appropriately plan and deliver the next session. Currently, manual methods adopted to analyse these student reflections are time consuming and tedious. This paper proposes a solution model that uses content mining and NLP techniques to automate the analysis of short self-reflections. We evaluate the solution model by studying its implementation in an undergraduate Information Systems course through a comparison of three different content mining techniques namely LDA–bigrams, GSDMM-bigrams, and Word2Vec based Clustering models. The evaluation involves both qualitative and quantitative methods. The results show that the proposed techniques are useful in discovering insights from the self-reflections, though the performance varied across the three methods. We provide insights into comparisons of the perspectives, which are useful to instructors.

The Association of Computing Machinery (ACM) and the Association for Information Systems (AIS) engaged in a project to revise the Information Systems Curriculum. The IS2010 model curriculum has been widely used for nearly a decade. However, its value may be decreasing as new approaches to model curricula have been introduced. The AIS and ACM established an exploratory taskforce which found there have been substantial changes in the IS field, and that current graduates' technical skills do not appear to meet industry needs. The IS discipline must express its core in terms of a standard curriculum that meet stakeholder demands. A joint ACM/AIS taskforce on the Information Systems Model Curriculum (IS2020) was created to develop new IS curriculum guidelines. This panel will introduce the work of the IS2020 taskforce. Panelists will present the key points from the final report. This session should be of interest to faculty and administrators developing college-level curricula in IS.

Constructive interactions through discussion forums allow students to open their horizons and thought processes to acquire more knowledge and develop skills. Thus, discussion forums play an important role in supporting learning. Additionally, the discussion forum provides the content for creating a knowledge repository. It contains discussion threads related to key course topics that are debated by the students. One approach to understanding the student learning experience is through the analysis of the discussion threads. This research proposes the application of discourse analysis and collaborative learning frameworks to discussion forums to gain further insights into the student's learning in a classroom. It is a foray into discourse analysis using in-class discussions. It demonstrates the application of Soller's framework and Penn Discourse Treebank (PDTB) to understand interactions at the discourse and semantic level. It also shows the use of unsupervised automated techniques to diagnose interactions in textual data. In this paper, we present an Integrated Discourse Analysis and Collaborative Learning Skills (IDALS) framework based on in-class discussions. We describe our experiences of applying IDALS framework and evaluating the solution model in a graduate in-class discussion forum. We also highlight the benefits of using visualizations to present the insights to the instructors.

This research full paper, describes knowledge management of class discussions using an analytics based framework. Discussions, either live classroom or through online forums, when used as a teaching method can help stimulate critical thinking. It allows the teacher to explore in-depth the key concepts covered in the course, motivates students to articulate their ideas clearly and challenge the students to think more deeply. Analysing the discussions helps instructors gain better insights on the personal and collaborative learning behaviour of students. However, knowledge from in-class discussions and online forums is not effectively captured and mined due to lack of appropriate automated tools. In this paper, the authors propose an automated discussion analysis (ADA) framework that provides a starting point providing guidance on the development of automated tools for performing different analysis on live classroom or online discussions. AI technology plays a key role in developing tools for knowledge representation and analysis. We propose software systems based on ADA framework and AI technology. The paper then describes a case study where one model of the ADA framework, individual behaviour analysis model, has been applied to automate the analysis of the online discussion forum used in a postgraduate course.

Our research full paper studies the perceptions of students and proposes technology design framework for smart class participation tools. Participation in classroom discussions has been observed to improve student comprehension and performance. In our contemporary tertiary educational context, student participation is characterised in mainly two forms; in-class discussions and online forums. Both forms of participation generate voluminous amounts of knowledge. However, the present difficulty in capturing and analysing these forms of participation leads to loss of knowledge and insights, which otherwise could be very useful. This study aims to analyse ways to improve data capture as well as data analysis of discussions. In this paper, we take a survey-based approach to study the students' perception of classroom discussions in terms of three components namely effective learning, technology support and grading. Based on the analysis of the survey results, we propose the design of a tool and the administrative requirements needed for in-class participation data capture and analysis. We present our results in the form of a framework for tool design and challenges to be addressed when using technology during class discussions.

Discussion forums play a key role in building knowledge repositories in an education institute. Asynchronous discussion forums enable part-time graduate professionals to have a better learning experience. This paper reports how a carefully curated discussion forum enhances the cognitive and social interactions among students in a graduate information systems course. In particular, we analyse the cognitive and social interactions and their impact on the student grades. To our surprise, the graduate students with their limited time resources, have higher order cognitive contributions and reasonable amount of social posts. We present the discussion forum design, cognitive and social behaviour analysis, grade analysis, and social network analysis. We use statistical methods and qualitative analysis to present our findings. Our findings provide useful insights that can be used in designing and implementing discussion forums in graduate business-technical courses.

This Innovative Practice full paper, describes the application of text mining techniques for extracting insights from a course based online discussion forum through generation of topic based summaries. Discussions, either in classroom or online provide opportunity for collaborative learning through exchange of ideas that leads to enhanced learning through active participation. Online discussions offer a number of benefits namely providing additional time to reflect and synthesize information before writing, providing a natural platform for students to voice their ideas without any one student dominating the conversation, and providing a record of the student's thoughts. An online discussion forum provides a repository comprising the discussion threads related to the topics discussed. One approach to extracting useful knowledge from the repository is through generation of concise summaries of the discussion for each topic. This summary information can help both the instructor and the student in being able to focus on the key learning points discussed in the forum threads. The focus of our research is directed towards analysis of online discussion forums (ODFs) and generating topic based summaries that can be viewed by both instructor and the students. We have developed a tool, Topic Based Summarization (TBS), that takes an excel sheet with discussion forum thread posts as input and generates visual reports of summaries that are clustered into different topics. We evaluate the tool using discussion thread posts for an undergraduate course titled “Business Process Modelling and Solutioning”. We also performed qualitative analysis of the tool to investigate the strengths and weakness of various summarization algorithms.

Discussion forums provide the base content for creating a knowledge repository. It contains discussion threads related to key course topics that are debated by the students. In order to better understand the student learning experience, the instructor needs to analyse these discussion threads. This paper proposes the use of clustering models and interactive visualizations to conduct a qualitative analysis of graduate discussion forums. Our goal is to identify the sub-topics and topic evolutions in the discussion forums by applying text mining techniques. Our approach generates insights into the topic analysis in the forums and discovers the students’ cognitive understanding within and beyond the classroom learning settings. We developed the analysis model and conducted our experiments on a graduate course in Information Systems. The results show that the proposed techniques are useful in discovering knowledge from the forums and generating user-friendly visualizations. Such results can be used by the faculty to analyse the students’ discussions and study the strengths and weaknesses of the students’ cognitive knowledge on course topics.

Reaching to young generation and attracting them to computing programs such as Information Systems (IS) and Computer Science (CS) is a key challenge faced by universities in Singapore. During their application process, many high quality students from junior colleges (JC) either don't choose IS program or choose IS program as the last option. School of Information Systems (SIS), Singapore Management University, decided to implement an innovative outreach program to reach and attract high quality JC students. A team of 30 faculty and staff worked on an outreach project to study and analyse the rationale behind the smaller numbers of applicants. We surveyed JC students and the results showed that only 5% of top JC students were aware of the term “Information Systems” and this seemed to be the root cause for low numbers and quality of applications to IS school. This shows that awareness is the first issue that should be tackled in our outreach. The outreach team unanimously agreed on executing an outreach camp which is designed to familiarize JC students on three main aspects; the value of IS in solving real world problems, common IS topics and IS related jobs. We have executed several outreach camps since our first run and have observed significant improvements in the number and quality of applications. In this paper, we share the gamified design of the outreach camp, results, challenges and lesson learnt in running an innovative IS outreach camp. We hope, our project will aid the university outreach teams in designing and organizing the outreach camps to the Gen-Z students.

Teacher evaluation is a vital element in improving student learning outcomes. Course and instructor feedback given by students, provides insights that can help improve student learning outcomes and teaching quality. Teaching and course evaluation systems help to collect quantitative and qualitative feedback from students. Since manually analysing the qualitative feedback is painstaking and a tedious process, usually, only the quantitative feedback is often used for evaluating the course and the instructor. However, useful knowledge is hidden in the qualitative comments, in the form of sentiments and suggestions that can provide valuable insights to help plan improvements in the course content and delivery. In order to efficiently gather, analyse and provide deeper insights from student feedback by topics, we developed a user-friendly application, Student Feedback Analysis Tool (SUFAT). The tool is an independent desktop application that can be installed and used by any nontechnical user. The tool takes an excel sheet with comments as an input and generates an excel sheet with visual reports of summaries that include sentiments and suggestions as an output. The tool can benefit instructors to quickly analyse the termly qualitative feedback and take appropriate actions. We intend to release the tool for public use so that instructors can download and install the tool on their computer system.

This Innovate Practice full paper, describes our experience in using the mini-case pedagogy to design and deliver a technical course, “Enterprise Integration” which is a year two course in the Information Systems undergraduate program. Case method is one approach that has been widely adopted in teaching many professions including law, medicine and business. The general practice is to ask students to read the case, usually four to eight pages long, prior to the session, and then spend the whole session discussing the case. However, for technology courses, the students are required to learn concepts and apply them using hands-on software tools through lab sessions. Hence, rather than use long cases, that cover the entire session, we need to use mini-cases that are usually one to two pages long. This pedagogy approach allows the class session to be designed with tight integration across the concept presentation lecture, mini-case analysis and hands-on lab session. We share the pedagogy implementation details and challenges, and discuss our findings and lessons learnt in designing and implementing the mini-case pedagogy. Thus providing one pathway for Information Systems and Computer Science professors to, implement the mini-case pedagogy in “technology heavy” courses.

Wikis are widely used collaborative environments as sources of information and knowledge. The facilitate students to engage in collaboration and share information among members and enable collaborative learning. In particular, Wikis play an important role in capstone projects. Wikis aid in various project related tasks and aid to organize information and share. Mining project Wikis is critical to understand the students learning and latest trends in industry. Mining Wikis is useful to educationists and academicians for decision-making about how to modify the educational environment to improve student's learning. The main challenge is that the content or data in project Wikis is unstructured in nature. The data formats are in both text and images. In this work, we propose an automated project Wiki mining solution that leverages data mining, text mining and optical character recognition techniques for discovering insights from Project Wikis. The results of mining process are presented as visual summaries which can be useful for the capstone project coordinators and academicians for education pedagogy decisions. We use dataset from Singapore Management University, School of Information Systems' undergraduate capstone projects for our solution evaluation. We evaluated our model on 314 capstone projects over a period of 8 years.

In this paper we report our study on the impact of implementing a flipped classroom model on student learning in an undergraduate introductory programming course. We use three components to measure student learning namely, final exam scores, competency acquisition and feedback levels. We compare a traditional offering with a flipped offering delivered the following year to a comparable student population, and with no change in the course content and assessments. We observed that in comparison to the traditional, the flipped model increased pass rates in the final exam and also enhanced competency acquisition. In terms of feedback levels, the flipped classroom provided more time for one-to-one in-class personalized feedback. Our approach is unique in the sense that we use a competency framework to be able to pin point the competencies that were improved as a result of the flipped model.

In academic institutions it is normal practice that at the end of each term, students are required to complete a questionnaire that is designed to gather students' perceptions of the instructor and their learning experience in the course. This questionnaire comprises of Likert-scale questions and qualitative questions. One of the important goals of this exercise is to enable the instructor and the senior management to examine the feedback and then enhance students' learning experience. In most universities, including our own, a lot of attention is paid to the quantitative feedback, which is summarized and statistical comparisons are computed, analysed and presented. However, the qualitative comments given by the students are not fully tapped. Capturing and analysing the qualitative feedback data, at the individual course, school and university-level, can provide valuable insights on teaching practices and curriculum. In this paper, we propose a conceptual framework for student feedback analysis that provides the necessary structure for implementing a prototype tool for mining student comments. We then discuss the application of the tool to analyse feedback from selected courses.

Real world information technology projects cut across multiple business domains and processes, involve large amounts of data and an assortment of different technologies. Advanced courses within an IS programs must include projects that help students gain a holistic view of an enterprise by exposing them to business domains, business processes and technical knowledge and skills that will help them design and deliver enterprise projects. In order to guide the instructor to effectively design and implement such enterprise project experiences, in this paper, we propose an enterprise integrated project environment (EIPE) framework based on business domains and business processes. Additionally, we share our experience in implementing this framework in the Data Warehousing and Business Analytics course. The analysis of the student feedback for this course demonstrates the benefits of EIPE in enhancing student learning experience. We share the project details and challenges and discuss our findings and lessons learnt in designing and implementing EIPE. Thus providing one pathway for Information Systems professors to, design an enterprise integrated project in their courses.

The overall aim of learning outcomes and competency based education is to improve the efficiency and effectiveness of higher education. It's important to track regularly student's competency acquisition so that the faculty can improve the teaching delivery and adapt the content accordingly. Student based self-assessment of competency tracking on a weekly basis aids faculty to intervene in course delivery process for effective teaching and learning experience. It also helps students to have a better understanding of their competency levels and accordingly prepare for weekly sessions. To enable students to self-assess the competencies in more structured format, there is a need for a systematic tracking tool. Additionally, it is also important for this tool to leverage on the latest technology for enhancing user friendliness and provide pervasive access through mobile technology. In this paper, we present a mobile web application, MyCompetencies, which enables faculty and students to track competencies on a weekly basis. We evaluated the tool in an undergraduate course over one semester comprising 110 students. We experiments showed that the tool helped the faculty to intervene and adapt the content to the students' learning pace.

SFIA skills framework is widely popular among education institutions and ICT industries. The framework provides ICT skills profiles which can be a valuable resource that supports the career planning of a student. However, currently a student does not have a method or approach to exploit the SFIA framework to align his or her competencies that he or she has acquired during the education program, to the skills defined in the SFIA framework. In this paper, we present a solution model for generating a skills report based on individual's competencies and experiences. In particular, we focus on Information Systems students' profiles. Student skill generator takes in curriculum and LinkedIn profile's data as an input and generates student's skills report which is aligned with SFIA framework. Moreover, the system also generates a list of recommended jobs in ICT sector that match the student's skills. We evaluated our solution model on an undergraduate core curriculum; Bachelor of Science (Information Systems Management) degree program BSc (ISM), offered by the School of Information Systems (SIS), Singapore Management University (SMU) and LinkedIn profiles of student from year 3, year 4 and alumni.

Human grading of introductory programming assignments is tedious and errorprone, hence researchers have attempted to develop tools that support automatic assessment of programming code. However, most such efforts often focus only on scoring solutions, rather than assessing whether students correctly understand the problems. To aid the students improve programming skills, effective feedback on programming assignments plays an important role. Individual feedback generation is tedious and painstaking process. We present a tool that not only automatically generates the static and dynamic program analysis outcomes, but also clusters similar code submissions to provide scalable and effective feedback to the students. We studied our tool on data from introductory Java programming assignments of year 1 course in School of Information Systems. In this paper, we share the details of our tool and findings of our experiments on 261 code submissions.

In this paper we report our study on the problem of competency acquisition when students progress from one course to another and more generally, from one term to the next. We observed that some students moved on to a second programming course without acquiring some of the competencies in the first programming course. This leads to problem in the second course, especially when these competencies are prerequisites for this course. We applied blended learning, which allows a student to learn at least in part through delivery of content and instruction via online media, to overcome this problem. Our approach is unique in the sense that we first assess student competencies and then develop targeted blended learning content to address competencies that have not been acquired in a pre-requisite course. We have applied the method to students doing the first year BSc Information Systems program.

Universities collect qualitative and quantitative feedback from students upon course completion in order to improve course quality and students' learning experience. Combining program-wide and module-specific questions, universities collect feedback from students on three main aspects of a course namely, teaching style, content, and learning experience. The feedback is collected through both qualitative comments and quantitative scores. Current methods for analyzing the student course evaluations are manual and majorly focus on quantitative feedback and fall short of an in-depth exploration of qualitative feedback. In this paper, we develop student feedback mining system (SFMS) which applies text analytics and opinion mining approach to provide instructors a quantified and exhaustive analysis of the qualitative feedback from students and avail insights on their teaching practices and this in turn will lead to improved student learning.

A fundamental artifact of any academic research is the data used as the basis of that research effort. A group of researchers, from institutions in multiple territories, has embarked on an ambitious research project that is aimed at enhancing the teaching of software engineering in four-year undergraduate programs. The research project details a set of workshops, for which the objective is the capture of data that will be the basis of the research effort. The first of these workshops was held in August 2011. The workshop comprised software engineering educators and representatives from the information technology industry. The data collection task sought to identify a set of topics that are considered suitable for teaching software engineering, along with identification of the years and depth at which these topics should be taught. The topics are derived from the outcome goals of the course/program.

This position paper proposes a framework for leveraging course competencies to effectively deliver and assess course content, and give valuable, timely feedback to students. The framework addresses the following five phases of a course, namely, content design, assessment design, content delivery and assessment, assessment feedback, and content review. The paper then presents the benefits of this approach and challenges in implementing this framework in scalable manner and suggests some solutions to overcome these challenges.

Since more than a decade, all kinds of businesses and organisations are intensively exploring enterprise-level information systems to better integrate their business processes, information flows and people. Consequently, the industry demands for technically skilled, but also “business-savvy” IT professionals are permanently growing. To meet this need, more and more computing education programs try to incorporate enterprise-level information systems into their curricula. While there is some computing education research done to investigate the need for this new type of IT-business professional and to analyse general implications for higher education, only very few research works or practice papers exist which report on concrete attempts to design and deliver higher education computing courses which intensively use enterprise-level systems. In this conference contribution, the authors report on a series of experiences made within the Bachelor of Science (Information Systems Management) degree program offered by the School of Information Systems (SIS) at the Singapore Management University (SMU). The primary focus of this paper is put on establishing a working set of best practices for the design of an effective structure of the face-to-face teaching sessions for courses which use enterprise-level systems and applications in their curricula. While this conference contribution is principally based on education experiences made within the frame of an Information Systems program, the best practices presented in this paper are equally applicable to any other computing education field or even to the engineering education in general.

Case studies have been used in different fields of university-level education already for decades. More recently, the advantages of using case studies have been realised by university-level computing educators, too. New approaches have been introduced in computing education - such as project-based learning, problem-based learning, situated learning or inquiry-based learning. Many of those approaches successfully use case studies. Despite the increasing popularity of this teaching methodology there seems to be a deep lack in any research papers or practice reports which would attempt to describe, evaluate and assess possible approaches or models in using case studies in computing education. This conference contribution reports on selected best practices of course design and delivery implemented in one of the core courses of the Bachelor of Science (Information Systems Management) degree program (BSc (ISM)) offered by the School of Information Systems (SIS) at the Singapore Management University (SMU). The paper presents, evaluates, compares and assesses four different course design and delivery models which are largely based on case studies and are extensively using this teaching methodology throughout the entire course lifecycle (starting with the course design process, delivery of face-to-face teaching sessions, student assessment process and post-mortem course review process).

The application of learning outcomes and competency frameworks have brought better clarity to engineering programs. Several frameworks have been proposed to integrate outcomes and competencies into course design, delivery and assessment. However, in many cases, competencies are course-specific and their overall impact on the curriculum is unknown. Such impact analysis is important for analyzing and improving the curriculum design. Unfortunately, manual analysis is a painstaking process due to large amounts of competencies across the curriculum. In this paper, we propose an automated method to discover their impact on the overall curriculum design. We provide a principled methodology for discovering the impact of courses’ competencies using Bloom’s Taxonomy and the learning outcomes framework.

While numerous Engineering education programs seem to have learning outcomes or competencies defined, in many cases, those learning outcomes or competencies do not have any practical relevance for the course design and delivery lifecycle. This conference contribution reports on curriculum and course design and delivery experiences made within the Bachelor of Science (Information Systems Management) degree program (BSc (ISM)) offered by the School of Information Systems (SIS) at the Singapore Management University (SMU). In particular, this paper focuses on the ongoing efforts to actively link program educational objectives, program level learning outcomes and course level competencies to the actual course design and delivery. Using a large third year core course of the BSc (ISM) program (called Enterprise Web Solutions course) as an example, this paper shows how the learning outcomes defined at the program level enable the cross-course alignment within the program, how the course level competencies support the inner-course content alignment, assessment component design and feedback delivery, and how the actual course delivery process enforces changes in course competencies and even changes in the program level learning outcomes.

Work by researchers in the area of simulation based instruction has shown that the visual presentation of the simulated domain in terms of emphasising domain concepts, changing the level of detail or reducing the complexity of a presentation plays an important role for effective instruction and knowledge transfer. Earlier approaches have tackled this important instructional aspect by creating several different simulations of the same domain or by manually assembling several views of the simulation in order to present instructionally valuable variations of the simulated domain to the trainee. In contrast to that, we propose a framework which builds on the concepts of data-enrichment of the domain simulation, a higher level descriptive definition and characterisation of different views and a query-based assembling mechanism for generating a specific view, thus automating the process of view generation. The implementation of this framework is achieved through the ADVISE (Adaptive Visual Simulation Environment) prototype system. The ADVISE system allows the characterisation and enrichment of domain simulation data and provides an integrated view-specification and view-visualisation suite. The underlying simulation model is based on the behaviourally described object approach and hence domain-independent.