Scaling up of Continual Professional Development for Mathematics Problem Solving in Singapore Schools

The more ambitious an educational innovation, the greater the challenge in scaling up. In this paper, we focus on the scaling up of an ambitious pedagogical practice—mathematics problem solving as a regular feature in the classroom. We adopt a long-term approach to continual professional development (CPD) that began with intensive work with one school before we broadened the programme to four other schools which span the spectrum of schools in Singapore. To evaluate this overall design, we examine the current state of each school’s capacity in sustaining mathematics problem-solving instruction. In particular, we study and report findings on these areas: the readiness of teachers, the instructional materials and supporting structures. Based on the findings, we reflect on our CPD strategies and our theory of action which guided the CPD programme.

     

Science Practicals in Schools: Are We Assessing Correctly?

Traditional practical examinations serve the purpose of ensuring that a candidate has some grounding in practical work. Regretably, however, they fall short in the promotion of process skills. A study done at the Institute of Education shows little relationship between tests which highlight these skills and the traditional practical tests. In today's context where emphasis should be placed on process rather than product, the continued dependence on traditional practical examinations does not seem to be in keeping with current curricula changes.     

Leading sustainable pedagogical reform with technology for student-centred learning: A complexity perspective

The literature on school improvement is littered with sombre reports of how ICT-mediated innovations have failed to create impact on teaching and learning. Even when evidence-based successes are palpable, they are sporadic and rarely sustainable. Against the backdrop of the litany of such studies, this paper reports the case of a primary school in Singapore that has a decade-long experience in integrating, growing and sustaining ICT-mediated innovations. By distilling the influences underpinning its integration, the article aims to make a contribution to the theorisation of educational leadership situated in the context of technology-mediated reform for student-centred learning. Using a complexity lens, this paper looks at how school leaders, together with other autonomous actors in its ecological system, foster the favourable conditions for sustainable technology-mediated pedagogical reform. Data of the study are drawn from interviews, observations of lessons, fieldtrips and professional development meetings as well as document analysis. Based on the findings, a complexity-informed model for technology-mediated reform is devised and its implications discussed. They include the need to cultivate the following within and across the subsystems of the school: (a) ecological awareness; (b) collective reflexivity on practices and implementations; (c) creating alignment; and (d) capacity to forge ecological coherence.     

Exploiting the categorical reliability difference for binary classification

In binary pattern classification, the reliabilities of statistics obtained from the samples of the two categories are generally different. When the statistics are used for modeling a classifier, such reliability difference could impact the generalization performance. We formulate a disparity index to show the statistical disparity based on the generalized eigenvalue decomposition of the categorical moment matrices. It is shown that this disparity index can effectively indicate the reliability difference between the two categories. The obtained reliability difference is subsequently utilized to adjust the regularization term of a classifier for effective learning generalization. Our experiments based on 10 real-world benchmark data sets validate the effectiveness of the proposed method.     

Comments from the Editor: The End of Teacher Evaluation?

Commentary

Comments from the Editor: The End of Teacher Evaluation?     

Twin SVM with a reject option through ROC curve

This paper proposes a new method which embeds a reject option in twin support vector machine (RO-TWSVM) through the Receiver Operating Characteristic (ROC) curve for binary classification. The proposed RO-TWSVM enhances the classification robustness through inclusion of an effective rejection rule for potentially misclassified samples. The method is formulated based on a cost-sensitive framework which follows the principle of minimization of the expected cost of classification. Extensive experiments are conducted on synthetic and real-world data sets to compare the proposed RO-TWSVM with the original TWSVM without a reject option (TWSVM-without-RO) and the existing SVM with a reject option (RO-SVM). The experimental results demonstrate that our RO-TWSVM significantly outperforms TWSVM-without-RO, and in general, performs better than RO-SVM.     

An investigation on the scientific thinking ability of fourth year university students

This paper reports on a study which examined the ability of a sample of fourth year university students to think scientifically when presented with a range of chemical phenomena. The main data collection instrument was the clinical interview. Each subject was interviewed in-depth for about one hour on a one-to-one basis. Each interview was taped, transcribed verbatim and then analysed. Five familiar chemical reactions ere used as foci for discussion in the interviews. For each reaction, each interviewee was asked, among other things, to make predictions about the overall energy change involved, and to make explanations as to why the change took place, i.e., the driving force for the change. The results show that the majority of the interviewees were using perceptually dominated thinking rather than conceptually dominated thinking; at the same time, they were unable to use science concepts consistently across the five reactions. It can thus be inferred that they were unable to think scientifically. Reasons for the lack of scientific thinking ability are explored and suggestions on alleviation of the problem are offered.     

Seamless learning in the mobile age: a theoretical and methodological discussion on using cooperative inquiry to study digital kids on-the-move

This paper shares the theoretical and methodological frameworks that are deployed in a 3-year study to examine how Singapore primary school students leverage on mobile technology for seamless learning. This notion of seamless learning refers to the integrated and synergistic effects of learning in both formal and informal settings, which is distributed across different learning processes (emergent or planned) as well as across different spaces (in or out of class). Drawing insights from the literature and our study, we assert that we should jettison the technology-centric view to adopt a socio-cultural framework. This move puts us in a better position to make sense of the rich complexities coalescing around the students’ in-situ use of mobile devices. We also critiqued the adequacy of our people-centred data collection method, in particular, cooperative inquiry in capturing instances of seamless learning. Challenges of operationalising the cooperative inquiry approaches are also detailed in the paper.     

Optimizing between data transformation and parametric weighting for stable binary classification

In this paper, an optimization problem is formulated for stable binary classification. Essentially, the objective function seeks to optimize a full data transformation matrix along with the learning of a linear parametric model. The data transformation matrix and the weight parameter vector are alternatingly optimized based on the area above the receiver operating characteristic curve criterion. The proposed method improves the existing means via an optimal data transformation rather than that based on the diagonal, random and ad-hoc settings. This optimal transformation stretches beyond the fixed settings of known optimization methods. Extensive experiments using 34 binary classification data sets show that the proposed method can be more stable than competing classifiers. Specifically, the proposed method shows robustness to imbalanced and small training data sizes in terms of classification accuracy with statistical evidence.