15 research outputs found
Dialogues in team-based learning's team readiness assurance tests: Are teams ready to dialogue?
No full textTeam-Based Learning (TBL) is a cooperative approach that has been widely adopted in higher education in recent years. One of its appeals is the claim to produce self-directed high performing learning teams. First developed by Larry Michaelsen in 1979, the approach asserts that by adhering to four essential elements, teams would increase the quality of peer talk for decision-making over time (Michaelsen et al., 2004).This study argues that there is a paucity of qualitative studies on the quality of peer talk in TBL. Qualitative studies are necessary to provide direct evidence and deep insights into how teams are making decisions. In particular, the quality of talk in the Team Readiness Assurance Test or tRAT phase (which is considered as the backbone of TBL) has mainly been inferred from indirect quantitative methods (Reimschisel et al., 2017). In addition, qualitative findings from other similar approaches suggest that some of the recommended tRAT processes may not foster quality peer talk with time.Likewise, the few TBL studies that have analysed team discourses also identified situational factors that continue to affect the quality of peer talk over time. However, these qualitative TBL studies either lack a coherent theoretical framework in characterising the quality of peer talk or have not involved the tRAT phase. Furthermore, their TBL implementations contain variations from the recommended process. While variation is an accepted practice within the TBL community (Compton et al., 2016), such freedom also makes interpretation challenging. Hence, further discourse studies would be needed to provide deeper insight into the quality of peer talk under different tRAT implementations.This study aims to address the above research gaps by adopting an embedded multiple case study (Stake, 2006) that follows the tRAT discussions of eight teams (n = 46) across three classes with varying practices. It seeks to uncover the types of talk these teams employ for decision-making during their second and fifth (last) tRAT sessions, how and why they employ the different types of talk.Using Wegerif’s (2013) dialogic theory of learning as the theoretical and analytical framework, the study finds that differences in views are necessary for high quality peer talk or exploratory talk. However, the requirement for teams to submit specific answers that are graded within a limited time motivated teams to predominantly adopt lower quality or cumulative talk for decision-making. This could occur even when the questions encouraged diverse views and when individual members had sufficient knowledge to engage each other dialogically. The study also offers some evidence that 1) feedback revealing dialogic gaps between the teams and the faculty, 2) symmetric peer relationships, 3) perception that dialogue is valuable for learning and 4) opportunities to appeal and improve grades could encourage exploratory talk during and after the tRAT.Based on these results, the study suggests the need to alleviate students’ concerns with grades and time, and to foster social norms that encourage symmetric peer relationships and a dialogic disposition towards learning so that students are ready to dialogue during the tRAT phase of TBL
Social network analysis of student interaction in team-based learning
No full textThis study aimed to explore the role of student interactions in Team-Based Learning (TBL)
by utilizing Social Network Analysis (SNA) to analyze student interactions in two undergraduate
calculus modules at NTU. The research involved surveys after each tRAT exercise, with
participants distributing 100 points among team members based on perceived contributions
levels. Data cleaning, imputation, and network generation were performed to generate social
network features, such as degree, betweenness, closeness and eigenvector centrality. A weak
positive correlation was found between individual contribution ratings and individual performance,
but with SNA features and weights transformation, a R2 value of 28.8% was achieved
with only four predictors in a linear model. This study suggests that individual performance is
positively correlated with pre-ability and participation in the collaborative nature of TBL, and
teams with stronger collaboration performed strongly, possibly due to the co-creation of understanding
within the team. This study also gives an example of how SNA was able to improve
the accuracy of models and how weights transformation could be used to improve linear models
involving SNA.Bachelor of Science in Physics and Mathematical Science
Adaptive Histograms And Dissimilarity Measure For Texture Retrieval And Classification
No full textHistogram-based dissimilarity measures are extensively used for content-based image retrieval. In an earlier paper [1], we proposed an efficient weighted correlation dissimilarity measure for adaptive-binning color histograms. Compared to existing fixed-binning histograms and dissimilarity measures, adaptive histograms together with weighted correlation produce the best overall performance in terms of high accuracy, small number of bins, no empty bin, and efficient computation for image classification and retrieval. This pape
Fuzzy Semantic Labeling for Image Retrieval
No full textThis paper proposes a fuzzy image labeling method that assigns multiple semantic labels together with confidence measures to each region in an image. The confidence measures are derived from the distance of the region to hyperplanes constructed by support vector machines. Test results show that this method yields higher classification accuracy and retrieval precision than crisp labeling methods that are based on crisp classification. 1
Measuring undergraduate students' reliance on generative AI during problem-solving: Scale development and validation
No full textReliance on AI describes the behavioral patterns of when and how individuals depend on AI suggestions, and appropriate reliance patterns are necessary to achieve effective human-AI collaboration. Traditional measures often link reliance to decision-making outcomes, which may not be suitable for complex problem-solving tasks where outcomes are not binary (i.e., correct or incorrect) or immediately clear. Therefore, this study aims to develop a scale to measure undergraduate students' behaviors of using Generative AI during problem-solving tasks without directly linking them to specific outcomes. We conducted an exploratory factor analysis on 800 responses collected after students finished one problem-solving activity, which revealed four distinct factors: reflective use, cautious use, thoughtless use, and collaborative use. The overall scale has reached sufficient internal reliability (Cronbach's alpha = .84). Two confirmatory factor analyses (CFAs) were conducted to validate the factors using the remaining 730 responses from this activity and 1173 responses from another problem-solving activity. CFA indices showed adequate model fit for data from both problem-solving tasks, suggesting that the scale can be applied to various human-AI problem-solving tasks. This study offers a validated scale to measure students' reliance behaviors in different human-AI problem-solving activities and provides implications for educators to responsively integrate Generative AI in higher education.Accepted versionRG 133/24ARC 1/24 Z
Battling Botpoop using GenAI for Higher Education: A Study of a Retrieval Augmented Generation Chatbots Impact on Learning
No full textGenerative artificial intelligence (GenAI) and large language models (LLMs) have simultaneously opened new avenues for enhancing human learning and increased the prevalence of poor-quality information in student response - termed Botpoop. This study introduces Professor Leodar, a custom-built, Singlish-speaking Retrieval Augmented Generation (RAG) chatbot designed to enhance educational while reducing Botpoop. Deployed at Nanyang Technological University, Singapore, Professor Leodar offers a glimpse into the future of AI-assisted learning, offering personalized guidance, 24/7 availability, and contextually relevant information. Through a mixed-methods approach, we examine the impact of Professor Leodar on learning, engagement, and exam preparedness, with 97.1% of participants reporting positive experiences. These findings help define possible roles of AI in education and highlight the potential of custom GenAI chatbots. Our combination of chatbot development, in-class deployment and outcomes study offers a benchmark for GenAI educational tools and is a stepping stone for redefining the interplay between AI and human learning.13 pages, 5 figures, SI with Annexes A, B and C upon reques
Learning Analytics for Bridging the Skills Gap: A Data-Driven Study of Undergraduate Aspirations and Skills Awareness for Career Preparedness
No full textAs the demands of the modern workforce evolve, universities are increasingly challenged to provide academic knowledge and the practical and transferable skills necessary for students’ career success. This study investigates the alignment between undergraduate students’ career aspirations, their perceived skill development, and the role of higher education institutions in bridging the skills gap. To address this issue, a comprehensive survey was conducted among undergraduate students to gather data on their career aspirations, their awareness of the skills required for their chosen careers, and their perceptions of how well their university supports their skill development. Using machine learning methods such as hierarchical clustering and k-nearest neighbors for classification, coupled with non-parametric statistical analysis such as the Mann–Whitney U and Chi-squared (χ2) tests to understand students’ perceptions of their career preparedness, the findings from this study provide valuable insights into how higher education institutions can prepare students for the workforce and highlight areas where improvements are needed to better support students in achieving their career goals
Author Correction: Carrier control in 2D transition metal dichalcogenides with Al2O3 dielectric
No full text10.1038/s41598-021-96557-4SCIENTIFIC REPORTS11
