Designing and delivering web application courses for computing undergraduates is a challenging task. Lack of understanding of web concepts affects the students” interest in web application development. Therefore., faculty employ several traditional strategies including hands on exercises and labs as well as innovative strategies such as videos and discussion forums. However., due to the volume of the posts in the forums., the instructors find it challenging to attend to the students' challenges and focus on more challenging topics across the classroom. In this paper., we propose a text mining based solution to extract the questions and complex topics. We evaluated the solution on the year 2 Web Application course offered to the computing undergraduates. Our experiments show that logistic regression model performs better in classifying question posts and cosine similarity performs better in assigning the topic label to the posts. The findings are visually depicted and are useful to the faculty to identify the topics that requires more attention to improve students' learning.

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.

In this study, we assess the efficacy of employing the ChatGPT language model to generate solutions for coding exercises within an undergraduate Java programming course. ChatGPT, a large-scale, deep learning-driven natural language processing model, is capable of producing programming code based on textual input. Our evaluation involves analyzing ChatGPT-generated solutions for 80 diverse programming exercises and comparing them to the correct solutions. Our findings indicate that ChatGPT accurately generates Java programming solutions, which are characterized by high readability and well-structured organization. Additionally, the model can produce alternative, memory-efficient solutions. However, as a natural language processing model, ChatGPT struggles with coding exercises containing non-textual descriptions or class files, leading to invalid solutions. In conclusion, ChatGPT holds potential as a valuable tool for students seeking to overcome programming challenges and explore alternative approaches to solving coding problems. By understanding its limitations, educators can design coding exercises that minimize the potential for misuse as a cheating aid while maintaining their validity as assessment tools.

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.

Cybersecurity education is considered an important part of undergraduate computing curricula, but many institutions teach it only in dedicated courses or tracks. This optionality risks students graduating with limited exposure to secure coding practices that are expected in industry. An alternative approach is to integrate cybersecurity concepts across non-security courses, so as to expose students to the interplay between security and other sub-areas of computing. In this paper, we report on our experience of applying the security integration approach to an undergraduate web programming course. In particular, we added a practical introduction to secure coding, which highlighted the OWASP Top 10 vulnerabilities by example, and demonstrated how to identify them using out-of-the-box security scanner tools (e.g. ZAP). Furthermore, we incentivised students to utilise these tools in their own course projects by offering bonus marks. To assess the impact of this intervention, we scanned students' project code over the last three years, finding a reduction in the number of vulnerabilities. Finally, in focus groups and a survey, students shared that our intervention helped to raise awareness, but they also highlighted the importance of grading incentives and the need to teach security content earlier.

With increasing data volume and adoption of technologies including machine learning and artificial intelligence across all industries, the demand for skilled Data Science professionals is continuing to increase globally. For educational institutions to teach the most up-to-date and industry-relevant skills and for businesses to hire employees with the right set of skills, it is important for them to stay tuned to the fast-changing dynamics of job landscape. In this research study, we present an NLP approach to the analysis of job listings from Glassdoor. Our solution mines insights on trending technical and soft skills in the Data Science job categories. Based on the insights, we provide recommendations to design overall data science curriculum learning outcomes (LOs). We also provide recommendations to the course designers on specific technical skills required for the topics of courses under the data science curriculum.

In this study, we showcase a design of an undergraduate Business Analytics track that integrates professional certifications from Amazon Web Services, Google, SAS, and Salesforce with core Business Analytics courses in an Information Systems undergraduate degree program. Certifications provide an excellent way for students to attain practical, experiential, and demonstrable skills which increasingly more employers look for in job candidates' portfolios. In close collaboration with industry partners, curriculum designers and faculty in institutions of higher learning can leverage high quality hands-on training materials provided by the certification vendors and align it with the core academic course content. Excellent teaching by the faculty combined with industry-relevant practical certification training is the recipe for a successful career building for Information Systems students.

This research paper presents a group project framework for a second-year programming course, which was conducted during the COVID-19 pandemic. The framework offers well defined stages of the group project which allow students to work on their choice of a real-world problem, integrate their learnings from previous courses, and present a working solution. In the group project, students actively participate, reflect, and contribute to achieving the goals set in the learning objectives of the course. Our framework incorporates key features from Kolb's Experiential Learning Theory (1984) and principles of active learning from Barnes (1989) to achieve active and experiential learning through active supervision. The use of group projects as a teaching pedagogy is widely adopted in many universities. Students work together, develop a plan, and demonstrate their abilities in building on existing knowledge acquired from previous courses, and apply them appropriately for problem solving. Prior to the pandemic, it was the norm for students to work on their group projects together by meeting physically on campus. Key benefits of working together physically are having the support of one another and the ease of communication. With the onset of the pandemic, safe distancing measures, and restrictions put in place have made it challenging for students to work on group projects together. During the pandemic, many courses were forced to move online with limited face-to-face learning opportunities on campus. This posed great challenges to the faculty in terms of effective supervision of students and their project progress. To mitigate the challenges, we devised a flexible strategy that makes use of both technology-based and non-technological means for monitoring students' group project milestones. The faculty receives continuous updates from students as they work towards each milestone. These milestones serve as important checkpoints for students. Continuous checks at different milestones help the faculty adopt appropriate intervention measures as issues arise. The group project learning framework consists of three main stages, namely Group Formation, Scoping of the Project, and Group Solutioning. The framework is overlaid with Kolb's Experiential Learning Theory concepts to describe the learnings, milestones, and deliverables of each stage. Each of these stages adopts Barnes's principles of active learning to enable active participation, reflection, and contribution by students. We evaluated the success of this framework through a comprehensive student survey analysis. The survey asked specific questions to students on all stages of the group project and the overarching component of teamwork and working online. We also present our findings and lessons learned for improvements of the framework. We believe that our framework will be valuable to educators in computing programs that wish to adopt effective supervision measures for group projects.

This innovative practice full paper describes our experiences of integrating information technology certifications into an undergraduate computing curriculum. As the technology landscape evolves, a common challenge for educators in computing programs is designing an industry-relevant curriculum. Over the years, industry practitioners have taken technology certifications to validate themselves against a base level of technical knowledge currently in demand in industry. Information technology (IT) certifications can also offer paths for academic computing programs to stay relevant to industry needs. However, identifying relevant IT certifications and integrating it into an academic curriculum requires a careful design approach as substantial efforts are needed by educators to design and deliver courses. This paper describes our proposed approach and experiences of identifying and mapping IT certifications for an undergraduate Information Systems (IS) curriculum. We first review our IS curriculum with an academic standard and an industry skills framework to identify relevant job roles for students. Next, we perform a web search in the Google Job Search engine for job postings relevant to these job roles and apply text analytics techniques to identify the IT certifications referenced in these job postings. We also share our implementation experiences of integrating two highly referenced IT certifications - Amazon Web Services (AWS) Cloud Practitioner and Solutions Architect - Associate certification exams in two undergraduate computing courses. We learnt that there is a need to allocate efforts for the faculty to manage the logistics matters. Students appreciate the teaching efforts and are motivated to train themselves for the certifications. In the first year of the implementation, we achieved 90% and 96% exam pass rates in AWS Cloud Practitioner certification and Solutions Architect - Associate certification, respectively. We hope that our approach and the lessons learnt can help other educators consider integrating IT certifications into their computing curricula.

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.

The 2020 Singaporean General Election (GE2020) was a general election held in Singapore on July 10, 2020. In this study, we present an analysis on social conversations about GE2020 during the election period. We analyzed social conversations from popular platforms such as Twitter, HardwareZone, and TR Emeritus.

In this study, we analyzed job roles and skills across industries in Singapore. Using social network analysis, we identified job roles with similar required skills, and we also identified relationships between job skills. Our analysis visualizes such relationships in an intuitive way. Insights derived from our analyses are expected to assist job seekers, employers as well as recruitment agencies wanting to understand trending and required job roles and skills in today's fast changing world.

In light of the #MeToo movement and publicized sexual harassment incidents in Singapore in recent years, we built an analytics pipeline for performing digital social listening on conversations about sexual harassment for AWARE (Association of Women for Action and Research). Our social network analysis results identified key influencers that AWARE can engage for sexual harassment awareness campaigns. Further, our analysis results suggest new hashtags that AWARE can use to run social media campaigns and achieve greater reach.

This study presents a social analytics approach to the study of public toilet cleanliness in Singapore. From popular social media platforms, our system automatically gathers and analyzes relevant public posts that mention about toilet cleanliness in highly frequented locations across the Singapore island - from busy shopping malls to food `hawker' centers.

In this study, we present a mobile gratitude journal application. Prior studies have shown that gratitude recording has a positive effect on human mental health. Our mobile gratitude journal application helps users record daily gratitude. It monitors and provides analytics insights on users' mood over time.

In this study, we analyze the phenomenon of fake news' spreading in the Internet. In recent years, the number of fake news and misinformation spreading cases increased. We analyze vaccine-related fake news spreading in Twitter and discover techniques used by the participants of fake news spreading. We present a simulation game designed to teach the public about the techniques behind fake news spreading.

In this study, we present an IoT-driven solution for human traffic management in a corporate cafe. Using IoT sensors, our system monitors human traffic in a physical cafe located at a large international corporation located in Singapore. The back-end system analyzes the streaming data from the sensors and provides insights useful to the cafe visitors as well as the cafe manager.

In this study, we analyze social conversations about Hong Kong Protests, a series of events that were widely seen and debated in social media in 2019. Our system collects data from Twitter and Reddit using their APIs. It performs sentiment analysis, and the analysis results show changes in public sentiment around major events. Social network analysis reveals influencers - users that are in the center of social conversations. Our interactive Tableau dashboard allows the user to easily monitor live social conversations about Hong Kong protests.

In this paper, we present a web browser plug-in for monitoring mental health. Unmanaged stress and depression can lead to suicidal ideation. Our web browser plug-in reminds users to adopt a positive attitude during their stressful work hours-by pushing positive content onto the web browser screen. It also tracks users' mental health by prompting them to indicate their emotions at different times throughout the day.

In recent years, the use of digital tools and technologies in educational institutions are continuing to generate large amounts of digital traces of student learning behavior. This study presents a proof-of-concept analytics system that can detect at-risk students along their learning journey. Educators can benefit from the early detection of at-risk students by understanding factors which may lead to failure or drop-out. Further, educators can devise appropriate intervention measures before the students drop out of the course. Our system was built using SAS®Enterprise Miner (EM) and SAS®JMP Pro.

In this research, we focus on the social phenomenon of suicide. Specifically, we perform social sensing on digital traces obtained from Reddit. We analyze the posts and comments in that are related to depression and suicide. We perform natural language processing to better understand different aspects of human life that relate to suicide.

Demand forecasting has been a challenging problem especially for products with short life cycles such as electronic goods and fashion items. Additionally, in the presence of limited past or historical data as well as the need for fast turnaround for forecast, producing timely and accurate demand forecast can be extremely challenging. In this study, we describe a cloud-based data gathering and processing system for intelligent demand forecasting.

The advent of the Web brought about major changes in the way people search for jobs and companies look for suitable candidates. As more employers and recruitment firms turn to the Web for job candidate search, an increasing number of people turn to the Web for uploading and creating their online resumes. Resumes are often the first source of information about candidates and also the first item of evaluation in candidate selection. Thus, it is imperative that resumes are complete, free of errors and well-organized. We present an automated resume evaluation tool called “CareerMapper”. Our tool is designed to conduct a thorough review of a user's LinkedIn profile and provide best recommendations for improved online resumes by analyzing a large number of online user profiles.

With the advent of the Internet and wide-spread popularity of online technology-enhanced learning platforms, many pedagogical activities today involve learners in online discussions such as synchronous chat. In this study, we describe a text mining method used for analyzing teamwork from such chat dialogue of students. The steps in the text mining method such as pre-processing and classification are described and the results of our analysis are presented in this paper.

This paper presents an analysis of star ratings in consumer reviews in Yelp, an online social platform for sharing consumer reviews about local businesses. In particular, we analyze consumer reviews about food businesses. We analyze how well or poorly the star ratings (on a scale of one star to five stars) associated with these reviews tally with the sentiment derived from the textual portion of the consumer review.

This study investigates and reports preliminary findings on player performance prediction approaches which model player's past performance and social diversity in mentoring network in Ever Quest II, a popular massively multiplayer online role-playing game (MMORPG) developed by Sony Online Entertainment. Our contributions include a better understanding of performance metrics used in the game and a foundation of recommendation systems for mentors and apprentices. We examined three different game servers from the Ever Quest II game logs. In all three servers, the results from our analyses suggest that increase in social diversity in terms of characters and classes encountered moderately negatively correlates with player performance. Based on this finding, we built predictive models to predict player's future performance based on past performance and social diversity in terms of mentoring activities. Our results indicate that 1) models employing past performance and social diversity perform better and 2) prediction for mentors is generally better than that for apprentices.

Massively Multiplayer Online Role-Playing Games (MMORPGs) have become increasingly popular and have communities comprising millions of subscribers. With their increasing popularity, researchers are realizing that video games can be a means to fully observe an entire isolated universe. In this study, we examine and report our findings on the effects of mentoring activities on player performance in Ever Quest II, a popular MMORPG developed by Sony Online Entertainment.