Mining learning preferences in web-based instruction: Holists vs. Serialists

Web-based instruction programs are used by learners with diverse knowledge, skills and needs. These differences determine their preferences for the design of Web-based instruction programs and ultimately influence learners' success in using them. Cognitive style has been found to significantly affect learners' preferences of web-based instruction programs. However, the majority of previous studies focus on Field Dependence/Independence. Pask's Holist/Serialist dimension has conceptual links with Field Dependence/Independence but it is left mostly unstudied. Therefore, this study focuses on identifying how this dimension of cognitive style affects learner preferences of Web-based instruction programs. A data mining approach is used to illustrate the difference in preferences between Holists and Serialists. The findings show that there are clear differences in regard to content presentation and navigation support. A set of design features were then produced to help designers incorporate cognitive styles into the development of Web-based instruction programs to ensure that they can accommodate learners' different preferences.This work is partially funded by National Science Council, Taiwan, ROC (NSC 98-2511-S-008-012- MY3; NSC 99- 2511-S-008 -003 -MY2; NSC 99-2631-S-008-001)

Investigating attributes affecting the performance of WBI users

This is the post-print version of the final paper published in Computers and Education. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.Numerous research studies have explored the effect of hypermedia on learners' performance using Web Based Instruction (WBI). A learner's performance is determined by their varying skills and abilities as well as various differences such as gender, cognitive style and prior knowledge. In this paper, we investigate how differences between individuals influenced learner's performance using a hypermedia system to accommodate an individual's preferences. The effect of learning performance is investigated to explore relationships between measurement attributes including gain scores (post-test minus pre-test), number of pages visited in a WBI program, and time spent on such pages. A data mining approach was used to analyze the results by comparing two clustering algorithms (K-Means and Hierarchical) with two different numbers of clusters. Individual differences had a significant impact on learner behavior in our WBI program. Additionally, we found that the relationship between attributes that measure performance played an influential role in exploring performance level; the relationship between such attributes induced rules in measuring level of a learners' performance

The effect of individual difference on learning performance using web-based instruction

Web-Based Instruction (WBI) brings a number of benefits to individuals requiring a combination of specific learning patterns and program structure. In this paper, we propose a WBI program which suits individual differences through an existing framework and which facilitates learning by accommodating learner preferences. In particular, we make advances in three key aspects. Firstly, we study three important individual differences (gender, cognitive style and prior knowledge) as well as their interactions in the resulting learning performances. Secondly, we combined three attributes to measure performance (gain score, number of visited pages and time spent on these pages) of the three interacting individual differences. Thirdly, we in-vestigate system features (navigation tools, additional support and content structure) to see how they can help users acquire information to meet their individual needs, resulting in an improvement in the learning performance. Two studies are presented; in one, we compare results from our program with previous studies thus evaluating its design. In the other, a data mining approach is used to investigate the effect of individual differences and how that could influence learner performance. Results indicate that performance can be affected by individual differences’ behaviour. Additionally, we found that the relationship between individual differences had an even higher impact on learners’ performance. The combined performance measurement attributes give a better understanding of how learners performed

O estilo cognitivo e a fixação de metasde aprendizagem em ambientes computacionais

Objective. To explore the influence of cognitive style, in the field dependence - independence dimension, on the setting, fixing and accuracy of learning goals, and at the same time to determine this influence on high school students’ expected learning achievement during their interaction with a hypermedia environment –called “Softri”– designed for solving problems related to rectangular triangles. Method. 85 tenth grade students from a state school in Bogotá, Colombia, took part in the study. The EFT (Embedded Figures Test) was used to measure cognitive style. Academic achievement was indicated by evaluations administered by the computational environment, and the goals selected by the students were registered by the software “Softri”. An Anova analysis was carried out to establish the presence of significant differences between academic achievement and goal setting for different groups of students, according to their cognitive style. Results. The results showed that independent field students set higher goals and are more accurate with respect to their expected learning achievements. Conclusion. It is possible to establish that independent field students have higher beliefs of control over their own learning process. They probably have higher internal locus of control. It is also possible for these students to have higher levels of academic self-efficacy since they set more demanding goals. These behaviors may be associated to a greater capacity for self-regulated learning.Objetivo. Explorar la influencia que ejerce el estilo cognitivo en la dimensión dependencia - independencia de campo sobre la fijación, ajuste y precisión de metas de aprendizaje. De igual manera, explorar dicha influencia en el logro de aprendizaje esperado en estudiantes de secundaria, durante su interacción en la resolución de problemas de triángulos rectángulos a través de un ambiente hipermedial denominado “Softri”. Método. En la investigación participaron 85 estudiantes del grado décimo de un colegio oficial de Bogotá. Se utilizó el EFT para medir el estilo cognitivo. El logro académico se obtuvo a través de evaluaciones realizadas en el escenario computacional. De igual forma, las metas seleccionadas por los sujetos eran registradas por el software ”Softri”. Para el tratamiento de los datos se realizó un análisis Anova, el cual permite establecer la existencia de diferencias significativas en cuanto a las medias del logro de aprendizaje y la formulación de metas entre los diferentes grupos de estudiantes de acuerdo con su estilo cognitivo. Resultados. Se mostró que los estudiantes independientes de campo se fijan metas más altas, siendo más precisos con respecto al logro de aprendizajes esperados. Conclusión. Es posible establecer que los estudiantes independientes de campo poseen altas creencias de control sobre su propio proceso de aprendizaje. Probablemente, poseen un locus de control interno alto. También es viable pensar que estos sujetos, poseen altos niveles de autoeficacia académica atendiendo a que se formulan metas más exigentes. Estas conductas pueden estar asociadas a una mayor capacidad de autorregulación del aprendizaje.Escopo. Explorar a influência que exerce o estilo cognitivo na dimensão dependência - independência de campo sobre a fixação, ajuste e precisão de metas de aprendizagem. Igualmente, explorar dita influencia no logro de aprendizagem esperado em estudantes de secundaria, durante sua interação na resolução de problemas de triângulos retângulos através de um ambiente hipermídia denominado “Softri”. Metodologia. Na pesquisa participaram 85 estudiantes do segundo ano de ensino médio de uma escola oficial de Bogotá. Foi utilizado o EFT para medir o estilo cognitivo. O logro acadêmico foi obtido através de avaliações feitas no cenário computacional. Do mesmo jeito, as metas selecionadas pelos sujeitos foram registradas pelo software “Softri”. Para o tratamento dos dados foi feita uma análise Anova, a qual permite estabelecer a existência de diferencias significativas em quanto às medidas do logro de aprendizagem e a formulação de metas entre os diferentes grupos de estudantes de acordo com seu estilo cognitivo. Resultados. Foi mostrado que os estudantes independentes de campo fixam metas mais altas, sendo mais precisos com respeito ao logro de aprendizagem esperado. Conclusão. É possível estabelecer que os estudantes independentes de campo têm altas crenças de controle sobre seu próprio processo de aprendizagem. Provavelmente, têm um locus de controle interno alto. Também é viável pensar que estes sujeitos têm altos níveis de autoeficácia acadêmica atendendo a que formulam metas mais exigentes. Estas condutas podem ser associadas a uma maior capacidade de autorregulação da aprendizagem

Evaluating student levelling based on machine learning model’s performance

In this paper, a novel application of machine learning algorithms is presented for student levelling. In multicultural countries such as UAE, there are various education curriculums where the sector of private schools and quality assurance is supervising various private schools for many nationalities. As there are various education curriculums in United Arab Emirates, specifically Abu Dhabi, to meet expats’ needs, there are different requirements for registration and success. In addition, there are different age groups for starting education in each curriculum. Every curriculum follows different education methods such as assessment techniques, reassessment rules, and exam boards. Currently, students who transfer to other curriculums are not correctly placed to their appropriate year group as a result of the start and end dates of each academic year as well as due to their date of birth, in which students who are either younger or older for that year group can create gaps in their learning and performance. In addition, pupils’ academic journeys are not stored which create a gap for the schools to track their learning process. In this paper, we propose a computational framework applicable in multicultural countries such as United Arab Emirates in which multi-education systems are implemented. Machine Learning are used to provide the appropriate student’ level aiding schools to provide a smooth transition when assigning students to their year groups and provide levelling and differentiation information of pupils for a smooth transition between one education curriculums to another, in which retrieval of their progress is possible. For classification and discriminant analysis of pupils levelling, three machine learning classifiers are utilised including random forest classifier, Artificial Neural Network, and combined classifiers. The simulation results indicated that the proposed machine learning classifiers generated effective performance in terms of accuracy

Integrating multiple individual differences in web-based instruction

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.There has been an increasing focus on web-based instruction (WBI) systems which accommodate individual differences in educational environments. Many of those studies have focused on the investigation of learners’ behaviour to understand their preferences, performance and perception using hypermedia systems. In this thesis, existing studies focus extensively on performance measurement attributes such as time spent using the system by a user, gained score and number of pages visited in the system. However, there is a dearth of studies which explore the relationship between such attributes in measuring performance level. Statistical analysis and data mining techniques were used in this study. We built a WBI program based on existing designs which accommodated learner’s preferences. We evaluated the proposed system by comparing its results with related studies. Then, we investigated the impact of related individual differences on learners’ preferences, performance and perception after interacting with our WBI program. We found that some individual differences and their combination had an impact on learners' preferences when choosing navigation tools. Consequently, it was clear that the related individual differences altered a learner’s preferences. Thus, we did further investigation to understand how multiple individual differences (Multi-ID) could affect learners’ preferences, performance and perception. We found that the Multi-ID clearly altered the learner’s preferences and performance. Thus, designers of WBI applications need to consider the combination of individual differences rather than these differences individually. Our findings also showed that attributes relationships had an impact on measuring learners’ performance level on learners with Multi-ID. The key contribution of this study lies in the following three aspects: firstly, investigating the impact of our proposed system, using three system features in the design, on a learner’s behavior, secondly, exploring the influence of Multi-ID on a learner’s preferences, performance and perception, thirdly, combining the three measurement attributes to understand the performance level using these measuring attributes

The influence of human factors on user's preferences of web-based applications: A data mining approach

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University on 20/12/2010.As the Web is fast becoming an integral feature in many of our daily lives, designers are faced with the challenge of designing Web-based applications for an increasingly diverse user group. In order to develop applications that successfully meet the needs of this user group, designers have to understand the influence of human factors upon users‘ needs and preferences. To address this issue, this thesis presents an investigation that analyses the influence of three human factors, including cognitive style, prior knowledge and gender differences, on users‘ preferences for Web-based applications. In particular, two applications are studied: Web search tools and Web-based instruction tools. Previous research has suggested a number of relationships between these three human factors, so this thesis was driven by three research questions. Firstly, to what extent is the similarity between the two cognitive style dimensions of Witkin‘s Field Dependence/Independence and Pask‘s Holism/Serialism? Secondly, to what extent do computer experts have the same preferences as Internet experts and computer novices have the same preferences as Internet novices? Finally, to what extent are Field Independent users, experts and males alike, and Field Dependent users, novices and females alike? As traditional statistical analysis methods would struggle to effectively capture such relationships, this thesis proposes an integrated data mining approach that combines feature selection and decision trees to effectively capture users‘ preferences. From this, a framework is developed that integrates the combined effect of the three human factors and can be used to inform system designers. The findings suggest that firstly, there are links between these three human factors. In terms of cognitive style, the relationship between Field Dependent users and Holists can be seen more clearly than the relationship between Field Independent users and Serialists. In terms of prior knowledge, although it is shown that there is a link between computer experience and Internet experience, computer experts are shown to have similar preferences to Internet novices. In terms of the relationship between all three human factors, the results of this study highlighted that the links between cognitive style and gender and between cognitive style and system experience were found to be stronger than the relationship between system experience and gender. This work contributes both theory and methodology to multiple academic communities, including human-computer interaction, information retrieval and data mining. In terms of theory, it has helped to deepen the understanding of the effects of single and multiple human factors on users‘ preferences for Web-based applications. In terms of methodology, an integrated data mining analysis approach was proposed and was shown that is able to capture users‘ preferences

The role of classifiers in feature selection: Number vs nature

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Wrapper feature selection approaches are widely used to select a small subset of relevant features from a dataset. However, Wrappers suffer from the fact that they only use a single classifier when selecting the features. The problem of using a single classifier is that each classifier is of a different nature and will have its own biases. This means that each classifier will select different feature subsets. To address this problem, this thesis aims to investigate the effects of using different classifiers for Wrapper feature selection. More specifically, it aims to investigate the effects of using different number of classifiers and classifiers of different nature. This aim is achieved by proposing a new data mining method called Wrapper-based Decision Trees (WDT). The WDT method has the ability to combine multiple classifiers from four different families, including Bayesian Network, Decision Tree, Nearest Neighbour and Support Vector Machine, to select relevant features and visualise the relationships among the selected features using decision trees. Specifically, the WDT method is applied to investigate three research questions of this thesis: (1) the effects of number of classifiers on feature selection results; (2) the effects of nature of classifiers on feature selection results; and (3) which of the two (i.e., number or nature of classifiers) has more of an effect on feature selection results. Two types of user preference datasets derived from Human-Computer Interaction (HCI) are used with WDT to assist in answering these three research questions. The results from the investigation revealed that the number of classifiers and nature of classifiers greatly affect feature selection results. In terms of number of classifiers, the results showed that few classifiers selected many relevant features whereas many classifiers selected few relevant features. In addition, it was found that using three classifiers resulted in highly accurate feature subsets. In terms of nature of classifiers, it was showed that Decision Tree, Bayesian Network and Nearest Neighbour classifiers caused signficant differences in both the number of features selected and the accuracy levels of the features. A comparison of results regarding number of classifiers and nature of classifiers revealed that the former has more of an effect on feature selection than the latter. The thesis makes contributions to three communities: data mining, feature selection, and HCI. For the data mining community, this thesis proposes a new method called WDT which integrates the use of multiple classifiers for feature selection and decision trees to effectively select and visualise the most relevant features within a dataset. For the feature selection community, the results of this thesis have showed that the number of classifiers and nature of classifiers can truly affect the feature selection process. The results and suggestions based on the results can provide useful insight about classifiers when performing feature selection. For the HCI community, this thesis has showed the usefulness of feature selection for identifying a small number of highly relevant features for determining the preferences of different users

Influence of Field Dependence / Independence, Gender, and Experience on Navigational Behavior and Configurational Knowledge Acquisition in a Desktop Virtual Reality Environment

Little is known about the influence of individual learner differences on navigational behaviors and learning within a desktop virtual reality environment (VE). This mixed-methods exploratory study used orienting, navigating, and wayfinding theory, digital performance-recording technology, and expert judges to examine the influences of the individual characteristics of field dependent/field independent cognitive style, gender, and prior domain knowledge or experience on navigation behaviors and survey knowledge acquisition of 30 police officers in a virtual crime scene created for the study. Detailed analyses were made of navigational moves and post-VE-treatment drawings of the virtual crime scene. Based on descriptive statistics, independent sample t-tests, analysis of variance, qualitative data, inter-judge reliability coefficients, and rating scores on post-treatment drawings, several conclusions were drawn: 1. Navigational behaviors in a desktop VE is individualistic rather than occupational. 2. IdentificaSchool of Teaching and Curriculum Leadershi