Taiyu Lin

A model for synchronous learning using the Internet

Improvements in technology and the increasing bandwidth of Internet access have led to an increasing popularity for synchronous solutions for instruction. Not only do they provide savings in terms of time and cost, in many situations they can also outperform both asynchronous online instruction and traditional face‐to‐face education. However, until now, the lack of a pedagogical framework for synchronous instruction has limited the effective use of this medium. This paper describes an online synchronous learning model that aims to provide guidelines for teachers and students to conduct synchronous instruction. The model provides a broad range of scenarios to suit individual requirements and covers both synchronous lecturing and ‘office‐hours’ modes.

An exploratory study of the relationship between learning styles and cognitive traits

To provide personalization and adaptivity in technology enhanced learning systems, the needs of learners have to be known by the system first. Detecting these needs is a challenging task and therefore, mechanisms that support this task are beneficial. This paper discusses the relationship between learning styles, in particular the Felder-Silverman learning style model, and working memory capacity, a cognitive trait. Due to this relationship, additional information about the learner is available and can be used to improve the student model. An exploratory study is presented to verify the identified relationship based on the literature. The results of the study show that the identified relationship between working memory capacity and two of the four dimensions of the learning style model is significantly supported. For the two remaining dimensions further research is required.

Analysing the Relationship between Learning Styles and Cognitive Traits

The need to provide more holistic adaptivity to students has brought us to investigate the relationship between learning styles and working memory capacity (WMC). The aim of this investigation is to study the relationship between learning styles and WMC in order to get additional information about the students. This information can be used to make more holistic adaptivity possible by improving the student modelling process of both learning styles and WMC. An experiment with 297 participants was conducted. Findings suggest that relationships from WMC to the active/reflective, the sensing/intuitive, and the visual/verbal learning styles exist, whereas the suggested relationship from WMC to sequential/global learning styles could not be found.

Adaptive Cognitive-Based Selection of Learning Objects.

Adaptive cognitive‐based selection is recognized as among the most significant open issues in adaptive web‐based learning systems. In order to adaptively select learning resources, the definition of adaptation rules according to the cognitive style or learning preferences of the learners is required. Although some efforts have been reported in literature aiming to update the adaptation logic used for a specific learner by updating his/her profile through the use of complex questionnaires that estimate the cognitive characteristics of learners, still the cognitive profile used for a learner remains static for a significant period, leading to the same selection decisions independent from the previous interactions of the learner with the system. In this paper, we address the learning object selection problem based on learners’ cognitive characteristics, proposing a cognitive‐based selection methodology that is dynamically updated based on the navigation steps of learners in a set of hypermedia objects. The proposed approach utilizes the Cognitive Trait Model, that is, an approximation model for learner’s cognitive capacity that provides a concrete method for identifying learner’s cognitive characteristics based on learners’ navigation steps. In our experiment we simulate different learner behaviors in navigating a hypermedia learning objects space, and measure the selection success of the proposed selection decision model as it is dynamically updated using the simulated learner’s navigation steps. The simulation results provide evidence that the proposed selection methodology can dynamically update the internal adaptation logic leading to refined selection decisions.

Dichotomic node network and cognitive trait model

In the search of creating a representation, such as a cognitive trait model, of cognitive traits, such as working memory capacity or inductive reasoning ability, of a learner, it is hard to find a consensus model of the cognitive trait among different perspectives of cognitive science. Dichotomic node network (DNN) is developed to provide a viable solution to this problem. DNN is a network representation of an entity of which the constituents are nodes that is consisted of a pair of dichotomic attributes. Through the contradiction detection mechanism and inclusion resolution mechanism, DNN is able to: (1) represents of an entity contains multiple portrayals/perspectives; (2) select appropriate portrayals for any particular entity is very difficult or impossible; (3) handle nonlinear aggregation of portrayals in which combinations does not render result linearly, and therefore very suitable for cognitive trait model, and is potential for other applications.

Cognitive Trait Modelling: The Case of Inductive Reasoning Ability.

Researchers have regarded inductive reasoning as one of the seven primary mental abilities that account for human intelligent behaviours. Researchers have also shown that inductive reasoning ability is one of the best predictors for academic performance. Modelling of inductive reasoning is therefore an important issue for providing adaptivity in virtual learning environments (VLEs). Research on Cognitive Trait Models (CTM) is currently underway to model such mental abilities by inferring learners’ behaviours in VLEs. Despite its recognized importance underlying the learning process of human beings, little effort is spent by the research community to support learners’ inductive reasoning process in such computer‐based learning environments. This paper provides a conceptual basis which addresses this issue by asking the question of how to model the inductive reasoning ability of a learner. A structural overview of CTM is first presented to give contextual information about modelling learner’s inductive reasoning ability, followed by examination of the characteristics of inductive reasoning ability in five different aspects: domain knowledge, generalization, working memory capacity, analogy, and hypothesis generation. On the basis of knowing the characteristics of inductive reasoning, extraction of the manifestations of inductive reasoning ability can then be made. The enumerated manifestations are then discussed.

Inductive Reasoning and Programming Visualization, an Experiment Proposal

We lay down plans to study how Inductive Reasoning Ability (IRA) affects the analyzing and understanding of Program Visualization (PV) systems. Current PV systems do not take into account the abilities of the user but show always the same visualization independently of the changing knowledge or abilities of the student. Thus, we propose IRA as an important skill when comprehending animation, which can be used to model the students and thus to adapt the visualization for different students. As an initial step we plan to check if IRA correlates with ability to answer program related questions during program visualization. We discuss the possible benefits of using IRA modeling in adaptive PV.

Semantic Relation Analysis and Its Application in Cognitive Profiling

Semantic web is an emerging paradigm that has great potential for the management of web content in a meaningful manner. With more and more semantic information appended to web, the web logs can be used to find valuable information about users. Their preferences, characteristics, cognitive capacity, or goals can be interpreted from the patterns of relations between traversed web nodes. A novel approach called semantic relation analysis (SRA) is proposed in this paper to harvest the new opportunities created by semantic web. In this paper, literature about navigational pattern analysis is presented. Example of using SRA in a learning system is also given. Empirical study about SRA showed promising results.