Ang, Rebecca P.

"This report presents the findings of the evaluation of the CARE Powercharged Program delivered to Secondary 1 Normal Technical students in three schools in 2005. ... In Singapore, there are few evaluations conducted on school-based intervention programs. It is necessary to evaluate these programs to ascertain if there are any impacts and what they are. At the same time, there is limited research on students in the Normal Technical stream. Evaluation and research are particularly important as 1) we need to understand students' learning needs, and 2) there is strong interest in understanding, strengthening and improving the learning experience of students in Normal Technical classes in the current policy climate."-- [p. 1] of executive summary.

This study examines the influence of both student and teacher perception of the student-teacher relationship on student's attitude towards teachers and school. It also seeks to explore any gender differences in the perception of teacher-student relationship between male and female adolescents. A sample of 1,266 students (541 girls and 725 boys) from six different middle schools in Singapore participated in this study. Findings indicated that gender differences were observed for certain dimensions in the teacher-student relationship predicting their attitude towards teachers and school. Possible explanations for the obtained results were suggested and implications of the findings were also discussed.

Express stream students rank amongst the top in international benchmarking comparisons in TIMMS and PISA, but those from the Normal Academic and Normal Technical streams obtain lower-than-average scores comparable to students from developing countries. Although a differentiated program has been specially tailored to cater to their pace of learning, many still fail to perform because educators may not have adequately considered the circumstances under which they are willing to participate and learn. Substantial research indicates that besides academics, a range of social, psychological, interpersonal and emotional factors also contribute to educational performance and achievement. To gain perspective on the respective contribution of multiple factors and encapsulate the systemic influences at individual and contextual factors on the long-term academic and non-academic trajectories of these students, this study uses a student engagement framework to unravel the educational challenges facing Normal stream students. Student engagement refers to a student’s active involvement in a task or activity and it captures the gradual process by which they connect with or disconnect from school. This framework describes students’ feelings (affective), behaviours and thoughts (cognitive) about their school experiences, and is predominantly used to understand student problems associated with significant academic or discipline problems and eventual school dropout in research situated in western contexts.

Outdoor education is emerging as a compulsory component of the school curriculum in Singapore. As more and more young people are involved in outdoor education programmes, the motivational factors that influence students’ participation in outdoor activities is an important area of inquiry. The purpose of this study was to use a self-determination theory framework to examine post course satisfaction level among young adolescents. A total of 314 secondary school students aged from 12 to 16 years took part in the survey. Results showed that external regulation negatively predicted self-reported satisfaction whereas intrinsic motivation positively predicted participants’ satisfaction levels of the course. It is highlighted that young adolescents should not be coerced into outdoor education programmes. They should be provided with a meaningful rationale for participation and given some autonomy for decision-making in order to have a more positive and enjoyable experience during the programme.

Youth violence is a growing concern in Singapore. To address this complex social issue, we surveyed the psychology, social science, and criminology literature to identify a total of 11 intrinsic (familial, individual, school) and 2 extrinsic (peer) factors linked to youth violence, and also their interdependencies. We then developed a complex agent network model where each complex agent is represented by a complex factor network of the 13 factors along with youth violence, coupled to each other through the extrinsic factors to form a complex social network. We simulated the model using as initial conditions the results from a large-scale school-based survey of the factors and random social ties. We find factors in each complex agent evolving with time under the influences from other factors, and the social ties between agents evolving with time as a result of behavioral imitation between agents. We ran a sensitivity analysis on the model, to find that the model is most sensitive to the parameters linking (1) non-intact family, (2) delinquency in general, (3) school disengagement, (4) peer delinquency, and (5) friends in gang to gang involvement. We also ran a series of intervention scenario simulations, and our results show that it is critical to intervene early, and successful interventions work by tipping the balance between competing intrinsic and extrinsic factors. Mental health professionals and school counsellors can then apply this unique insight from the model to design more effective interventions.

How well adolescents get along with others such as peers and teachers is an important aspect of adolescent development. Current research on adolescent relationship with peers and teachers is limited by classical methods that lack explicit test of predictive performance and cannot efficiently discover complex associations with potential non-linearity and higher-order interactions among a large set of predictors. Here, a transparently reported machine learning approach is utilized to overcome these limitations in concurrently predicting how well adolescents perceive themselves to get along with peers and teachers. The predictors were 99 items from four instruments examining internalizing and externalizing psychopathology, sensation-seeking, peer pressure, and parent-child conflict. The sample consisted of 3232 adolescents (M = 14.0 years, SD = 1.0 year, 49% female). Nonlinear machine learning classifiers predicted with high performance adolescent relationship with peers and teachers unlike classical methods. Using model explainability analyses at the item level, results identified influential predictors related to somatic complaints and attention problems that interacted in nonlinear ways with internalizing behaviors. In many cases, these intrapersonal predictors outcompeted in predictive power many interpersonal predictors. Overall, the results suggest the need to cast a much wider net of variables for understanding and predicting adolescent relationships, and highlight the power of a data-driven machine learning approach with implications on a predictive science of adolescence research.