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Articles 1 - 15 of 15
Full-Text Articles in Education
Can Engagement Be Compared? Measuring Academic Engagement For Comparison, Ling Tan, Xiaoxun Sun, Siek Toon Khoo
Can Engagement Be Compared? Measuring Academic Engagement For Comparison, Ling Tan, Xiaoxun Sun, Siek Toon Khoo
Dr Ling Tan
Student engagement is a reflection of active involvement in learning. In digital learning environment, research studies on engagement have been focused on detecting behavioral and psychological engagement indicators from the patterns of activities using feature engineering, but student engagement estimates were rarely compared across sessions or across domains of learning. This paper describes how this could be done by revisiting engagement instrument, diagnosing engagement indicators, estimating engagement parameters, and equating. This study illustrates how engagement reliability can be improved by refining engagement indictors. We demonstrated through DataShop data that student engagement levels can be compared across domains of learning.
Pyp And Myp Student Performance On The International Schools’ Assessment (Isa), Ling Tan, Yan Bibby
Pyp And Myp Student Performance On The International Schools’ Assessment (Isa), Ling Tan, Yan Bibby
Dr Ling Tan
This study, undertaken by the Australian Council for Educational Research (ACER), investigated how International Baccalaureate (IB) students enrolled in the Primary Years Programme (PYP) and Middle Years Programme (MYP) performed on the International Schools’ Assessment (ISA), relative to non‐IB students. The ISA assesses student performance in Grades 3 to 10 across four domains: Math Literacy, Reading, Narrative Writing, and Expository Writing. The math and reading components of the assessment are based on the reading and mathematical literacy frameworks of the HOECD'sProgramme for International Student Assessment (PISA)H. The study sample included IB students (N=23,575) and non‐IB students ( …
Fit-To-Model Statistics For Evaluating Quality Of Bayesian Student Ability Estimation, Ling Tan
Fit-To-Model Statistics For Evaluating Quality Of Bayesian Student Ability Estimation, Ling Tan
Dr Ling Tan
Bayesian ability estimation is a statistical inferential framework constructed from a measurement model and a prior knowledge model. It is attractive in practice because Bayesian estimation methods offer an elegant way to incorporate appropriate knowledge on target ability distribution in order to improve the accuracy of ability estimation, when there are uncertainties or errors in observable data. One hurdle for applying Bayesian-based methods is evaluating the validity of Bayesian ability estimates at individual-level. This study investigated a class of fit-to-model statistics for quantifying the evidence used in learning Bayesian estimates. The relationship between fit-to-model statistics and root mean square error …
Performance Comparison Between Ib School Students And Non-Ib School Students On The International Schools’ Assessment (Isa) And On The Social And Emotional Wellbeing Questionnaire, Ling Tan, Yan Bibby
Performance Comparison Between Ib School Students And Non-Ib School Students On The International Schools’ Assessment (Isa) And On The Social And Emotional Wellbeing Questionnaire, Ling Tan, Yan Bibby
Dr Ling Tan
This report examines the performance of students enrolled in the IB Primary Years Programme (PYP) and the IB Middle Years Programme (MYP) on the ACER International Schools’ Assessment (ISA) compared with non-IB students from the same ISA cohorts. The ISA is an assessment created especially for students in international schools in Grades 3 to 10. The assessment asks both multiple-choice and open-ended questions in the areas of writing, reading and mathematics, and provides international normative information about student performance. The Reading and Mathematical Literacy are based on the internationally endorsed frameworks of the OECD's Programme for International Student Assessment (PISA).
Mobile Intelligence, Ling Tan, L Yang, A Waluyo, J Ma, B Srinivasan
Mobile Intelligence, Ling Tan, L Yang, A Waluyo, J Ma, B Srinivasan
Dr Ling Tan
Cutting-edge research and application issues on computational intelligence in the mobile environment The combination of mobile computing and computational intelligence, Mobile Intelligence focuses on learning patterns and knowledge from data generated by mobile users and mobile technology. As a very new area of research, mobile intelligence has created a wide range of opportunities for researchers, engineers, and developers to create new applications for both end users and businesses. Mobile Intelligence covers the comprehensive state-of-the-art in various applications of computational intelligence to the mobile paradigm, including mobile data intelligence, mobile mining, mobile intelligence security, mobile agent, location-based mobile information services, mobile …
Technical Report Of Brunei Nsscme 2008, Andrew Stephanou, Ling Tan
Technical Report Of Brunei Nsscme 2008, Andrew Stephanou, Ling Tan
Dr Ling Tan
A collaboration between ACER and the Ministry of Education in Brunei Darussalam aims to establish an assessment monitoring system in government schools, NSSCME (National Study of Student Competencies in Mathematics and English), and to assist in building test development and psychometrics capacity. ACER’s input is in the examination of the Brunei curriculum, development of assessment instruments in line with the curriculum, construction of reporting scales, and training of staff in item writing, marking, interpretation of results, educational measurement and use of data analysis software. A comprehensive workshop program for item writers, teachers and officers is being conducted both in Brunei …
Isa Technical Report, Ling Tan, Yan Bibby
Adaptive Estimated Maximum-Entropy Distribution Model, L Tan, D Taniar
Adaptive Estimated Maximum-Entropy Distribution Model, L Tan, D Taniar
Dr Ling Tan
The Estimation of Distribution Algorithm (EDA) model is an optimization procedure through learning and sampling a conditional probabilistic function. The use of conditional density function permits multivariate dependency modelling, which is not captured in a population-based representation, like the classical Genetic Algorithms. The Gaussian model is a simple and widely used model for density estimation. However, an assumption of normality is not realistic for many real-life problems. Alternatively, the maximum-entropy model can be used, which makes no assumption of a normal distribution. One disadvantage of the maximum-entropy model is the learning cost of its parameters. This paper proposes an Adaptive …
Maximum-Entropy Estimated Distribution Model For Classification Problems, L Tan, D Taniar
Maximum-Entropy Estimated Distribution Model For Classification Problems, L Tan, D Taniar
Dr Ling Tan
Classification is a fundamental problem in machine learning and data mining. This paper applies a stochastic optimization model to classification problems. The proposed maximum entropy estimated distribution model uses a probabilistic distribution to represent solution space, and a sampling technique to explore search space. This paper demonstrates the application of the proposed maximum entropy estimated distribution model to improve linear discriminant function and rule induction methods. In addition, this paper compares the proposed classification model with decision trees. It shows that the proposed model is preferable to decision tree C4.5 in the following cases: i) when prior distribution of classification …
A Clustering Algorithm Based On An Estimated Distribution Model, L Tan, D Taniar, K Smith
A Clustering Algorithm Based On An Estimated Distribution Model, L Tan, D Taniar, K Smith
Dr Ling Tan
This paper applies an estimated distribution model to clustering problems. The proposed clustering method makes use of an inter-intra cluster metric and performs a conditional split-merge operation. With conditional splitting and merging, the proposed clustering method does not require the information of cluster number and an improved cluster vector is subsequently guaranteed. In addition, this paper compares movement conditions between inter-intra cluster metric and intra cluster metric. It proves that, under some conditions, the intersection of convergence space between inter-intra cluster metric and intra cluster metric is not empty, and neither is the other subset in the convergence space. This …
Adaptive Estimation Of Distribution Algorithm With Maximum Entropy Principle, Ling Tan, D Taniar, K Smith
Adaptive Estimation Of Distribution Algorithm With Maximum Entropy Principle, Ling Tan, D Taniar, K Smith
Dr Ling Tan
No abstract provided.
Parametric Optimization In Data Mining Incorporated With Ga-Based Search, L Tan, D Taniar, K Smith
Parametric Optimization In Data Mining Incorporated With Ga-Based Search, L Tan, D Taniar, K Smith
Dr Ling Tan
A number of parameters must be specified for a data-mining algorithm. Default values of these parameters are given and generally accepted as ‘good’ estimates for any data set. However, data mining models are known to be data dependent, and so are for their parameters. Default values may be good estimates, but they are often not the best parameter values for a particular data set. A tuned set of parameter values is able to produce a data-mining model of better classification and higher prediction accuracy. However parameter search is known to be expensive. This paper investigates GA-based heuristic techniques in a …
A Taxonomy For Inter-Model Parallelism In High Performance Data Mining, Ling Tan, D Taniar, K Smith
A Taxonomy For Inter-Model Parallelism In High Performance Data Mining, Ling Tan, D Taniar, K Smith
Dr Ling Tan
No abstract provided.
Dynamic Task Assignment In Server Farms: Better Performance By Task Grouping, Ling Tan, Z Tari
Dynamic Task Assignment In Server Farms: Better Performance By Task Grouping, Ling Tan, Z Tari
Dr Ling Tan
This paper describes a dynamic load balancing approach to distributed server farm systems. This approach overcomes the interference caused by non-negligible very-large tasks in the heavy-tailed distribution. First, a subset of tasks is allocated proportionally to the processing capability of participating servers by taking into account their remaining processing time. Later, tasks in the servers are processed in order of priority to optimise the system response time. The proposed load balancing algorithm also takes into account the information on server loads to avoid load imbalance caused by very large tasks. The experiments show that the mean waiting time and the …
A New Parallel Genetic Algorithm, Ling Tan, D Taniar, K Smith
A New Parallel Genetic Algorithm, Ling Tan, D Taniar, K Smith
Dr Ling Tan
One problem of propagating the globally fittest individual via neighbourhood evolution in both the island model and the cellular model of existing parallel genetic algorithms (PGAs) is that the migration of the globally best individual is delayed to non-adjacent processors. This may cause an inferior search in those sub-populations. The propagation delay of the globally best individual is proportional to the network distance between two processors. Delayed migration of the best individual in PGAs is an essential deviation from the sequential version of the genetic algorithm, in which the best individuals are always used to compete with other individuals. To …