Social and Behavioral Sciences Commons^™

Retention In Enhanced Team Based Learning Course: Retain Or Refrain?, Janil Puthucheary, Sok H. Goh, Tam C. Ha, Doyle G. Graham, Sandy Cook

Faculty of Social Sciences - Papers (Archive)

Students’ ability to retain content in medical school has always been a concern. At Duke-NUS Medical School, we modified our Team-Based Learning (TBL) classes known as TeamLEAD, a learning strategy for first year basic science content, to include an open/closed-book option in the readiness assurance phase to engage teams in deeper discussion. We hypothesize that the open-book option allows students to engage in deeper learning in their teams, which leads to an improvement in retention ability for each individual student at the end of their first year basic science curriculum.

Methods: A total of 115 MCQs used throughout first year …

A Multidisciplinary Learning Experience Contributing To Mental Health Rehabilitation, Lorna Moxham, Christopher F. Patterson, Ellie K. Taylor, Dana J. Perlman, Susan Sumskis, Renee M. Brighton

Faculty of Science, Medicine and Health - Papers: part A

Purpose People who access health services often have a range of needs that require the involvement of members from a multidisciplinary team. Teaching future health professionals about the importance of a multidisciplinary approach can be challenging. The aim of this paper is to describe a project called Recovery Camp that enhanced multidisciplinary health education through experiential and immersive engagement with people experiencing mental illness.

Method Future health professionals and people with a lived experience of mental illness took part in Recovery Camp - an innovative five-day therapeutic recreation initiative in the Australian bush. Results are presented in a case study …

Joint Development Of Teacher Cognition And Identity Through Learning To Teach L2 Pronunciation, Michael S. Burri, Honglin Chen, Amanda Ann Baker

Faculty of Social Sciences - Papers (Archive)

The constructs of teacher cognition and teacher identity have recently gained considerable attention in second language teacher education research for their crucial roles in understanding teacher learning. While a number of current studies have examined the contributions of both constructs, the connections between cognition and identity are yet to be fully conceptualized. This article addresses this gap by drawing on the notion of identification to examine the identity construction and cognition development of 15 student teachers in the context of a postgraduate course on pronunciation pedagogy. Questionnaires, focus group interviews, observations, and semi-structured interviews were triangulated to obtain an in-depth …

Ontological Learner Profile Identification For Cold Start Problem In Micro Learning Resources Delivery, Geng Sun, Tingru Cui, Jun Shen, Dongming Xu, Ghassan Beydoun, Shiping Chen

Faculty of Engineering and Information Sciences - Papers: Part B

No abstract provided.

Vdes J2325-5229 A Z = 2.7 Gravitationally Lensed Quasar Discovered Using Morphology-Independent Supervised Machine Learning, Fernanda Ostrovski, Richard G. Mcmahon, Andrew J. Connolly, Cameron A. Lemon, Matthew W. Auger, Manda Banerji, Johnathan M. Hung, Sergey E. Koposov, Christopher E. Lidman

Faculty of Engineering and Information Sciences - Papers: Part B

We present the discovery and preliminary characterization of a gravitationally lensed quasar with a source redshift z_s = 2.74 and image separation of 2.9 arcsec lensed by a foreground z_l = 0.40 elliptical galaxy. Since optical observations of gravitationally lensed quasars show the lens system as a superposition of multiple point sources and a foreground lensing galaxy, we have developed a morphology-independent multi-wavelength approach to the photometric selection of lensed quasar candidates based on Gaussian Mixture Models (GMM) supervised machine learning. Using this technique and gi multicolour photometric observations from the Dark Energy Survey (DES), near-IR JK photometry …

A Framework Of Mlaas For Facilitating Adaptive Micro Learning Through Open Education Resources In Mobile Environment, Geng Sun, Tingru Cui, Wanwu Guo, Shiping Chen, Jun Shen

Faculty of Engineering and Information Sciences - Papers: Part B

No abstract provided.

Sbar: A Conceptual Framework To Support Learning Path Adaptation In Mobile Learning, Alva Hendi Muhammad, Jun Shen, Ghassan Beydoun, Dongming Xu

Faculty of Engineering and Information Sciences - Papers: Part B

No abstract provided.

A Comparison Study For Supervised Machine Learning Models In Cancer Classification, Huaming Chen, Hong Zhao, Lei Wang, Jiangning Song, Jun Shen

Faculty of Engineering and Information Sciences - Papers: Part B

No abstract provided.