Mental health

Powering Peer Support

cdtlsd Lighting Talks , , , , ,

Melissa ZEHNDER and Duane ONG

Singapore Institute of Technology (SIT) 

melissa.zehnder@singaporetech.edu.sg, duane.ong@singaporetech.edu.sg

Zehnder, M., & Ong, D. (2024). Powering peer support [Lightning talk]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-mzehnder-dong/

SUB-THEME

Opportunities from Wellbeing

KEYWORDS

Opportunities from Wellbeing, student peer support, wellbeing, student wellbeing, mental health, student engagement

CATEGORY

Lightning Talk

EXTENDED ABSTRACT

Students in university experience significant stress, they have to navigate and adapt to new environments and adopt new behaviours. At the Singapore Institute of Technology (SIT), we aim to provide holistic and extensive student care and support. To this end, a Student Development and Care Strategy has been established to ensure a safe, vibrant and supportive campus community. This framework encompasses aspects of student development, integration, care and recreation, to boost their learning journey beyond academic rigour. Throughout each academic year, there is continuous student engagement and support. This creates a common campus vocabulary on good mental health habits and normalises help-seeking behaviours. Often, this is accomplished by harnessing the power of peer-to-peer support. 

 

In 2022, SIT embarked on a peer-to-peer emotional support programme. Potential supporters were interviewed for the programme, before they embarked on specially curated training with five core modules. To help drive ground-up initiatives, a student executive committee was installed. As a group, these peer supporters engage the student community to promote available support (theirs included) and resources for good mental health. Some challenges faced include lack of student awareness of the presence of peer support and their willingness and knowledge of how to connect with a peer supporter, as well as understanding the benefits of peer support. 

 

The next phase of the peer support service has begun, where the student peer supporters should be gainfully engaged and students seeking the support report reaping benefits. 

 

Feedback from other stakeholders such as SIT faculty will also be consolidated. The presentation will share a summary of the Student Development and Care Strategy, objectives of the SIT peer support programme, challenges faced and ideas to navigate these challenges, success stories, as well as ideas for the future. If possible, the presentation will have both SIT staff and student peer supporters sharing their peer support experiences. 

 

Abolishing 11:59PM Deadlines or: How Students Learned to Stop Worrying and Love the Assignments

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Prasanna Karthik Vairam

Department of Computer Science, School of Computing, NUS

prasanna@comp.nus.edu.sg

Vairam, P. K. (2024). Abolishing 11:59PM deadlines or: How students learned to stop worrying and love the assignments [Paper presentation]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-pkvairam/

SUB-THEME

Opportunities from Wellbeing

KEYWORDS

Assignment deadlines, mental health, github classrooms, version control

CATEGORY

Paper Presentation 

EXTENDED ABSTRACT

University courses typically have many assignments, and the deadlines are predominantly set at a fixed time (e.g., 11:59 PM on Friday). While we know that students despise deadlines through anecdotal evidence, contemporary research also agrees (Capelle, 2023). Fixed deadlines result in anxiety, stress, and induce last-minute mishaps (e.g., accidental deletion). Inability to perform well in an assignment can result in frustration and shame, negatively affecting the student’s learning journey through the rest of the course. Some university teachers have experimented with suggested deadlines, wherein, students are allowed to submit late for a penalty. However, such solutions do little to alleviate the problems, considering that students often work last-minute and submit sub-par works (Castro, 2022). Therefore, there is a need for a solution that promotes student wellness without compromising on submission quality.

 

In this paper, we describe a continuous submission methodology that eliminates the need for a fixed Assignment deadline in a computer programming course, promoting student wellness without compromising learning outcomes.

 

We achieve this through a combination of i) Github Classroom, a cloud-based education technology platform from Microsoft, ii) code templates, a technique that we propose to transform assignments into fill-in puzzles, and iii) Conferring, a known pedagogical technique. Github classroom uses git to maintain versions of the files in the project. This means that students can incrementally save (or commit) their work to the platform. Every save is a submission. Next, to make the process of solving assignments interesting and learning outcome-focused, we provide code templates, which is a pre-written code given by the instructor, with blanks that the student is required to fill. Pre-written code is the boring piece of code that must be written for the program to run, while it does not contribute to the learning outcome. Last, the instructor confers with students to monitor their progress continuously. The saves (or commits) on Github classroom allows the instructor to identify individual students progress over time, providing an opportunity to intervene (and confer) if necessary. Conferring can be done either face-to-face or through Github Issues. Github Issues is a feature typically used to file software bugs, but they come in handy as a non-intrusive and less-intimidating way of reminding students that they are falling behind on the assignment.

 

The process of coding and submitting assignments could be as follows:

  1. Day 0: Students accept the assignment through the Github classroom link created by the instructor.
  2. Day 1: Download the code template (i.e., the starter code) from Github classroom.
  3. Day 1: Fill in the missing piece of code. For instance, the code corresponding to Q1 of Assignment.
  4. Day 3: After finishing Q1, perform a git commit (save and submit).
  5. Day 5: Instructor looks at student progress across class and identifies those falling behind. Instructor files Github Issues for these students. This feedback can either be a gentle reminder or could be little hints to guide them in the correct direction.
  6. Day 7: Student continues to code Q2 and performs git commit.
  7. Eventually, the student finishes Q3 and Q4 of student, each with a different git commit.
  8. The instructor does not need to check the deadline since each git commit comes with a timestamp.
  9. No penalties are given as long as the commits are within an acceptable timeline.

The effectiveness of the method can be observed by looking at the commit (or save) distribution over time and the number of commits made by the students (not adding the graph due to pending IRB clearance).

CONCLUSION

The proposed method removes the anxiety that students typically associate with assignments, allowing them to focus on the learning outcomes instead. The method is both a continuous submission and continuous evaluation/monitoring system. Some of the other benefits include including the prevention of accidental deletion, since all data is stored in cloud every time the students execute the git commit. Although we demonstrate our method in the context of a programming course, it can be extended to courses in other domains.

REFERENCES

Capelle, J. D., Senker, K., Fries, S., & Grund, A. (2023). Deadlines make you productive, but what do they do to your motivation? Trajectories in quantity and quality of motivation and study activities among university students as exams approach. Frontiers in Psychology, 14, 1224533. https://doi.org/10.3389/fpsyg.2023.1224533

Castro, F. E. V., Leinonen, J., & Hellas, A. (2022), Experiences with and lessons learned on deadlines and submission behavior, Proceedings of the 22nd Koli Calling International Conference on Computing Education Research, https://doi.org/10.1145/3564721.3564728

Analysing Mental Health Discourse on Mindline.Sg

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Johnathan YAP Wen Jie  

Health and Wellbeing

john.yap@nus.edu.sg

Yap, J. (2024). Analysing mental health discourse on Mindline.sg [Poster presentation]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore.https://blog.nus.edu.sg/hecs/hecs2024-jyap/

SUB-THEME

Opportunities from Wellbeing 

KEYWORDS

Mental health, sentiment analysis, web scraping, topic modelling 

CATEGORY

Poster Presentation

BACKGROUND  

In June 2020, Singapore launched mindline.sg, an anonymous digital mental health resource website. As part of its resources, the platform includes let’s talk, a community forum promoting open mental health discussions and offering resources, education, and support among local students. 

OBJECTIVES

This poster aims to textually analyse discussions within mindline.sg’s let’s talk forum, focusing on identifying prevalent wellbeing themes through analysing sentiment patterns and topic modelling. By understanding these aspects, this poster hopes to derive insights that will inform strategies for promoting mental wellness and enhancing psychological safety among students

METHODS 

Data Collection and Preprocessing

let’s talk was selected as the primary data source for this poster due to its role as a public forum for mental health discussions among local students. 

A total of 1,086 unique forum posts (from the ‘Ask a Therapist’ section) were systematically collected using web scraping techniques with Python (i.e., BeautifulSoup and Selenium), covering the period from June 2022 to June 2024

Pre-processing text normalisation techniques such as lowercasing, stop words removal and tokenisation were applied to standardise the textual data for further analysis. 

Sentiment Analysis

Sentiment analysis was conducted to assess the emotional tone of these forum discussions. The analysis utilised the VADER (Valence Aware Dictionary and Sentiment Reasoner) sentiment analysis tool provided by NLTK (Natural Language Toolkit). The VADER sentiment analyser computed sentiment scores for each post, generating a compound score that reflects overall sentiment polarity (positive, neutral, or negative). 

Posts were categorised based on their compound sentiment score: 

  • Positive: Posts with a compound score >= 0.05. 
  • Negative: Posts with a compound score <= -0.05. 
  • Neutral: Posts with compound scores between -0.05 and 0.05 
Topic Modelling

To uncover the primary themes discussed in the forum posts, this poster utilised Latent Dirichlet Allocation (LDA), a widely used probabilistic model in natural language processing. LDA identifies clusters of words that frequently occur together across posts, assuming these clusters represent coherent topics. 

RESULTS 

Sentiment analysis (see Figure 1) revealed a range of emotional tones in forum discussions, including positive, neutral, and negative sentiments. The prevalence of negative sentiments, which was 21% higher than positive and 59% higher than neutral, suggests that the anonymity of the let’s talk forum provides a safe space for participants to openly express their challenges and emotional distress. This anonymity likely fosters a sense of psychological safety, enabling students to discuss sensitive topics without fear of judgment or repercussion.

Figure 1. Sentiment Analysis of Forum Posts 

The temporal analysis (see Figure 2) revealed spikes in negative sentiments, particularly in January 2024 (i.e., start of a new school term) and mid-April 2024 (i.e., exam periods). These spikes suggest heightened stress and challenges faced by students during these periods, which are critical for informing targeted interventions and support strategies. 

Figure 2. Temporal Sentiment Analysis

Using Latent Dirichlet Allocation (LDA), five key themes were identified in forum discussions: 

  • Personal Experiences and Emotions: Topics included discussions about work, social interactions, and daily life. 
  • Emotional Expression and Relationships: Topics centered on feelings of loneliness, social connections, and personal identity. 
  • Existential Reflections and Growth: Discussions focused on life reflections, personal development, and aspirations. 
  • Family Dynamics and Personal Challenges: Themes explored familial relationships, school experiences, and personal responsibilities. 
  • Seeking Advice and Support: Posts often sought advice, support, and shared personal challenges. 

Figure 3. Isolation Forest (anomaly detection). 

An Isolation Forest algorithm (see Figure 3) was employed to detect anomalous posts within the dataset. By analysing features such as sentiment scores, post length, and content structure, it flagged posts with unusual patterns, suggesting potential extreme emotional responses that could indicate crisis situations requiring further attention. 

In total, 10 posts were flagged as anomalous, representing 1.1% of the total posts. One notable example included a detailed account from a student describing verbal and physical abuse from her mother, underscoring the serious nature of some flagged content. 

CONCLUSION AND FUTURE DIRECTIONS 

This poster examined discussions from mindline.sg’s let’s talk forum using sentiment analysis and topic modelling, highlighting prevalent negative sentiments and key thematic clusters among students. Future directions could explore the dynamics and interactions between different users within the forum, leveraging network analysis to understand community support structures and identify influential contributors. These insights could further inform intervention strategies and enhance the efficacy of mental health support initiatives in educational contexts. 

REFERENCES

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3(Jan), 993-1022. 

Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. arXiv preprint cs/0205070. 

Liu, F. T., Ting, K. M., & Zhou, Z. H. (2008, December). Isolation forest. In 2008 eighth ieee international conference on data mining (pp. 413-422). IEEE. 

Weng, J. H., Hu, Y., Heaukulani, C., Tan, C., Chang, J. K., Phang, Y. S., … & Morris, R. J. (2024). Mental wellness self-care in Singapore with mindline. sg: A tutorial on the development of a digital mental health platform for behavior change. Journal of Medical Internet Research, 26, e44443.