introduction: in this study, the authors aimed to examine the effects of cooperative learning methods using web quest and team-based learning on students’ self-direction, self-regulation, and academic achievement. methods: this is a comparative study of students taking a course in mental health and psychiatric disorders. in two consecutive years, a group of students were trained using the web q...

firms invest in their learning activities to improve their innovative efforts. these kinds of activities are either internal or external. knowledge which flows among local actors is one of the external learning factors. spontaneous knowledge flows among firms without any compensation or with compensation less than the real value is called knowledge spillover. this paper analyzes the effect of k...

A boosting-based ensemble learning can be used to improve classification accuracy by using multiple classification models constructing to cope with errors obtained from preceding steps. This paper presents an application of the boosting-based ensemble learning with penalty setting profiles on automatic unknown word recognition in Thai. Treating a sequential task as a non-sequential problem requ...

The landscape contest provides a new and configurable framework to evaluate the robustness of supervised classification techniques and detect their limitations. By means of an evolutionary multiobjective optimization approach, artificial data sets are generated to cover reachable regions in different dimensions of data complexity space. Systematic comparison of a diverse set of classifiers high...

Previous research suggests that adolescents with learning disabilities (LD) are less accurate in predicting academic performance than normally achieving (NA) adolescents and display a tendency to overestimate their level of performance (e.g., Klassen, 2007). However, no studies have been conducted investigating whether this overestimation is specific to academic contexts or a phenomenon that ex...

Machine Learning methods for Performance Prediction in Intelligent Tutoring Systems (ITS) have proven their efficacy; specific methods, e.g. Matrix Factorization (MF), however suffer from the lack of available information about new tasks or new students. In this paper we show how this problem could be solved by applying Transfer Learning (TL), i.e. combining similar but not equal datasets to tr...

Reinforcement learning agents have traditionally been evaluated on small toy problems. With advances in computing power and the advent of the Arcade Learning Environment, it is now possible to evaluate algorithms on diverse and difficult problems within a consistent framework. We discuss some challenges posed by the arcade learning environment which do not manifest in simpler environments. We t...

The purpose of this study was to synthesize the findings from 23 articles that compared the mathematical and cognitive performances of students with mathematics learning disabilities (LD) to (a) students with LD in mathematics and reading, (b) age- or grade-matched students with no LD, and (c) mathematical-ability-matched younger students with no LD. Overall results revealed that students with ...

One of the most important features of “intelligent behaviour” is the ability to learn from experience. The introduction of Multiagent Systems brings new challenges to the research in Machine Learning. New difficulties, but also new advantages, appear when learning takes place in an environment in which agents can communicate and cooperate. The main question that drives this work is “How can age...