An investigation on the correlation of learner styles and learning

Educ Inf Technol DOI 10.1007/s10639-014-9371-3

An investigation on the correlation of learner styles and learning objects characteristics in a proposed Learning Objects Management Model (LOMM) Supachanun Wanapu & Chun Che Fung & Nittaya Kerdprasop & Nisachol Chamnongsri & Suphakit Niwattanakul

# Springer Science+Business Media New York 2014

Abstract The issues of accessibility, management, storage and organization of Learning Objects (LOs) in education systems are a high priority of the Thai Government. Incorporating personalized learning or learning styles in a learning object management system to improve the accessibility of LOs has been addressed continuously in the Thai education system. A proposed Learning Object Management Model (LOMM) is discussed in this paper which aims to adapt and optimize the learning process based on characteristics of the individual learners. This study aims to find the correlation between learning styles and LOs characteristics in the LOMM. Decision Tree and Apriori algorithms were used to generate a predictive model for the classification of learners. Development of the predictive model was based on survey results from 1,586 high school students in Nakhon Ratchasima province, Thailand. The diverse LOs characteristics were analyzed in order to find the correlation with learning styles of the learners. The classification model consists of 24 sub-models used to predict a learner’s class based on 8 groups of LOs characteristics. The best accuracy S. Wanapu : N. Chamnongsri : S. Niwattanakul (*) School of Information Technology, Institute of Social Technology, Suranaree University of Technology, 111 University Avenue, Muang District, Nakhon, Ratchasima 30000, Thailand e-mail: [email protected] S. Wanapu e-mail: [email protected] N. Chamnongsri e-mail: [email protected] C. C. Fung School of Engineering and Information Technology, Murdoch University, South Street, Murdoch, WA 6150, Australia e-mail: [email protected] N. Kerdprasop School of Computer Engineering, Suranaree University of Technology, Nakhon, Ratchasima 30000, Thailand e-mail: [email protected]

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obtained in the study was 80.23%. Finally, for the next phase this approach has been designed to support the proposed LOMM and it is expected that it could be readily applied to other e-learning systems and digital repositories. Keyword Classification model . Decision tree . Learning styles . Learning objects

1 Introduction Rapid developments of Information and Communication Technology (ICT) have impacted all fields, and in particular, the discipline of learning and teaching has been enhanced greatly by the widely use of multimedia technology (Lee 2012; Wang et al. 2011). The Government of Thailand has also passed National Education Act in 2010 (Ministry of Education 2010) aiming to improve multimedia technology and to encourage research and development of the use of modern education technologies in education among other issues. Instructors from all education sectors in Thailand, are encouraged and motivated to develop content and to use electronic media as much as possible. This has resulted in the development and production of a vast volume of educational materials in different formats such as audio, video, animation, power point presentation, simulation, and so on. While these Learning Objects (LOs) are important online materials for teachers and students, there is a lack of standardization and description of these LOs, even though they all have similar characteristics such as reusability, share-ability, and interoperability (Wiley 2000). On the other hand, a survey conducted by the National Statistical Office in 2008 found that only 41.6% of the instructors used electronic media for their teaching. Some of the reasons of this low level of utilization are due to the lack of infrastructure and difficulties involved with the management and access to the LOs. Therefore, the issues of accessibility, management, storage and organization of the LOs in the Thai education system should be considered. Approaches to the issue of LOs storage mainly belong to two groups. The first group of schools, educational institutes, and universities adopt the approach to develop eLearning systems and digital repository systems for LOs storage by using open source software, mainly due to the budgetary limitation. The popular Learning Management System (LMS) and Digital Repository System are Moodle (2013) and DSpace (2013), respectively. The other group comprises mainly governmental departments or organizations such as various ministries, which develop specific Learning Object Repository (LOR) for the storage of LOs. Examples of such development are the Media Education Center, The School Net, Thailand Knowledge Center or TKC (2013), and TCU:GLOBE (2013). In addition, there is a problem with the e-Learning and digital repository’s systems’ ability in searching or retrieving appropriate LOs for individual learners. This is due to the fact that there is no mechanism in the LOR to identify the different learning styles of the users, and to match the learners to appropriate LOs. For example, if a student intends to search for particular LOs in the e-Learning system, the results will rely on the search function of the LMS, which are mostly based on keywords. If a student wants to search for LOs in the digital repository system, the results will be based on metadata standards and the input provided by the learner. In the case of searching for LOs in the

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LOR developed by governmental departments, organizations, or various ministries, most likely the results are based on keywords and directory. From literature, many reports have proposed answers to address the above-mentioned problem. For example, studies on the impact of personalized learning or learning styles have looked at enhancing the quality of learning system and encourage the students to take advantage from the LOs (Baki and Çakıroğlu 2010; Chang et al. 2009; Dağ and Geçer 2009; de Boer et al. 2011; Kay and Knaack 2008). Semantic web technologies have been used to improve the online learning environments and bridge the gap between learners and instructors (Arch-int and Arch-int 2013; Jovanović et al. 2007). Moreover, decision tree techniques have been suggested for the development of web-applications based on personalized requirements and enhancing the efficiency of the learning environment (Chen 2008; Lin et al. 2013; Lu et al. 2007). Another proposal is the development of recommendation systems to map the multimedia format and topics of the LOs to the learners, and to provide suggestions of related LOs to repeat visitors (Chen and Persen 2012; Klašnja-Milićević et al. 2011; Ocepek et al. 2013; Vesin et al. 2013). However, although the above systems have incorporated personalized learning or learning styles into a learning object management system to improve the accessibility of LOs (Knight et al. 2005; Verbert et al. 2006), very few studies have integrated data mining technique to find the correlation between learning styles and characteristics of the LOs. One of the desirable features of problem is to provide keyword search capability based on individual learner’s characteristics, and this feature will improve the management of the LOs search system. Matching between learning styles and LOs characteristics will enhance the performance of the system by providing ranked LOs based on the preferences of the learners, and this will reduce the access time and will also improve the accuracy of matching. Therefore, this study aims to develop a methodology to find the correlation between learning styles and LOs characteristics, which forms the basis of a proposed Learning Object Management Model (LOMM) for the matching of ranked LOs to the different learning styles of the users. The proposed model is designed to cover the features of LOs, extending beyond just the multimedia format of material (Ocepek et al. 2013). The proposed correlation model is also applicable to other e-learning systems and digital repositories for retrieval and management of educational materials. The rest of this paper is organized as follows. In Section 2, related works on learning styles, LOs characteristics, decision tree and recommendation systems for learning and teaching are described. Section 3 presents the proposed LOMM architecture. Section 4 describes the research procedure to find the correlation between learning styles and LOs characteristics. Section 5 discusses the results of the following experiments: feature selection and grouping, development of the classification model, and evaluation of the predictive model. Finally, the conclusion and discussion on future research are given in Section 6.

2 Related works 2.1 Learning styles The concept of learning style was first defined by Rita Dunn in 1960 (Boydak 2001). Definitions of learning styles have been established from various perspectives.

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Honigsfeld and Dunn (2006) defined learning style as a biological and developmental set of personal characteristics that make the identical instruction effective for some students and ineffective for others. The popular mentioned learning style is a set of student’s individual characteristics that are reflected in his or her learning behavior such as how a student learns and likes to learn, how a student interacts with the learning environment, and how an instructor adapts to individual student for successful teaching (Chang et al. 2009; Keefe 1987; Reiff 1992; Tseng et al. 2008). Learners have different learning styles and this fact should be considered in the selection of the most effective mode of instruction for the learners (Marković and Jovanović 2012; Pashler et al. 2009). Some researchers believed that there are many reasons to incorporate or integrate learning style with instructional technology because such information on the learning style provides useful information about the differences of the individuals (Amira and Jelas 2010; Grasha and Yangarber-Hicks 2000; Montogomery and Groat 1998). From many reports, it is evidenced that an understanding of learning styles can lead to improvement of the learning environments. This study, therefore, aims to develop a methodology to find the correlation between learning styles and LOs characteristics in the proposed Learning Object Management Model (LOMM). Many learning styles inventories have been developed for studying learners’ behaviors under various contexts and requirements. A well established and widely accepted approach by Grasha and Riechmann (1974) defined learning styles based on the social context of students in thinking and interaction with other students in different classroom environments and experience. The six learning styles are categorized as follows: Independent: The ones who prefer to work alone and need a little direction or attention from the lecturer. Avoidant: The ones who are shy and uninterested in learning, and with poor work and study organization. Dependent: The ones who depend heavily on lecturer and friends in learning task. Collaborative: The ones who find group work enjoyable. Participative: The ones who are attentive and responsive to coursework requirements. Competitive: The ones who emphasize on high grades and attention from lecturers. In this study, the inventory of Grasha-Reichmann Learning Style Scale (GRSLSS) is used because it was especially designed to be used for high school students (Baykul et al. 2010). It is also most often used for classification of the learning styles of Thai students (Maneenil et al. 2010; Thonthai 2009). 2.2 Learning object characteristics Wiley (2000) defined LOs as “any digital resource that can be reused to support learning”. In 2002, the definition of LOs by the IEEE (2002) is given as “any entity, digital or non-digital, that may be used for learning, education or training”. Since then, LOs characteristics have been defined in many ways and approaches for practical implementation. Several educational metadata standards, such as IEEE LOM (IEEE 2002), Dublin Core (DCMI 1995), IMS consortium (IMS 2001), and SCORM (ADL 2004), provided standard specifications of LOs in order to facilitate accessibility,

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interoperability, and reusability. At the same time, developments of LOs based on ontologies and web standards such as XML, RDF, and OWL, have also been carried out for the management of learning environments. The Abstract Learning Object Content Model (ALOCoM) was designed to facilitate repurposing learning object content (Verbert et al. 2004). The ALOCoM ontology has been included to differentiate between Content Fragments (CFs), Content Objects (COs) and Learning Objects (LOs). The ALOCoM ontology has been used in many research projects for improving the online learning environments and bridging the gap between learners and instructors (Jovanović et al. 2007; Knight et al. 2005). With respect to this study, the LOs characteristics are designed to describe the features of the LOs, beyond just the multimedia format, and follow the metadata standards. ALOCoM ontology that focuses on the content of the LOs has been used as part of the definition of the LOs characteristics in this paper. 2.3 Decision tree There are various algorithms for predictive modeling in the Data Mining discipline for dealing with classification problems. Examples of such algorithms are Artificial Neural Networks (ANN), Support Vector Machines (SVMs) and Decision Tree (DT). ANN is a popular approach widely used to solve classification problems. However, ANN’s relative importance of potential input variables, long training processes, and interpretative difficulties have often been criticized. SVM has high performance in classification problems, however, the rules obtained by the SVM algorithm are hard to understand directly (Zhang and Zhao 2003). DT is a basic form of supervised learning and it represents one of the most popular approaches for classification problems. However, a disadvantage of DT is that it only handles discrete attributes (Quinlan 1986, 1993) and it does not allow multiple output attributes. Nonetheless, many researchers have used DT to propose and develop web-applications based on personalized requirements and enhanced the efficiency of the learning environment (Chen 2008; Lin et al. 2013; Ocepek et al. 2013). DT does have several advantages over other approaches. For example, the results of the DT classification model can be interpreted and converted to a set of decision rules easily and it can also classify both categorical and numerical data. In addition, DT does not require a long training time and subsequently this will save the modeling time while dealing with large datasets (Zhao and Zhang 2008). In this study, the predictive models derived are expected to be readily applied to other e-learning systems and digital repositories. In addition, as the data on the learning styles obtained from GRSLSS will be grouped into categories, the classification approach by using DT is therefore most suitable to address the requirement for analysing the correlation between learning styles and LOs characteristics. 2.4 Recommendation system for learning and teaching Many recommendation systems have been proposed to integrate learning styles in the process. Özpolat and Akar (2009) proposed an automatic learner modeling approach based on diagnosing and classifying the Felder–Silverman learning styles. The NBTree classification was used to classify the learners based on their interests. However, the

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modeling approach was used only to select the content of the data objects selected by the learner, and it did not support keyword mapping. Chen and Persen (2012) presented the AnnForum as a recommendation system for the establishment and sharing of collaborative knowledge. AnnForum could recommend content-based topics that learners can focus on. However, AnnForum was originally intended for recommending messages in educational discussion forums and it has not been implemented for searching in traditional discussion forums. It also did not deal with issues on LOs characteristics and learning styles. The Programming Tutoring System (Protus) was proposed as a tutoring system in 2011. Protus was based on principles of learner style identification and content recommendation for course personalization using an ontology for building the structure of semantic web. However, it was shown in 2013 that Protus needs personalization rules in order to derive further inferences (Klašnja-Milićević et al. 2011; Vesin et al. 2013). Ocepek et al. (2013) provided a model to recommend various multimedia content types for different individuals using decision tree. The results found that students prefer different multimedia learning materials among them. However, the proposed model only focused on comparing the multimedia types such as animation, video, simulation, audio, and text, and made no reference to the content and characteristics of the LOs. Based on the above, there are already a few recommendation systems that select the mode of learning styles for high school students. They also attempt to provide a match between the learning styles and the LOs characteristics, which cover the object content. Such work lead to the development of semantic search algorithm and for LOs storage and management.

3 Proposed LOMM architecture The proposed Learning Object Management Model (LOMM) (Wanapu et al. 2014a) was established in order to find the correlation between learning styles and LOs’ characteristics. The goal is to develop a semantic web application, which is capable of adapting and optimizing the learning process based on the characteristics of the individual learners and the learning objects. The model has been designed and developed using a modular approach. Figure 1 shows the architecture and the main system modules which are given below: & &

&

Learning Styles Module (LS Module): It is a module for selecting the characteristics of learners based on registered data that identify the learner’s profile and learning styles. Learning Objects Characteristics Module (LOsC Module): It is a module for selecting the features of LOs characteristics from the Knowledge Base of Science Learning Objects (SLOs). SLOs was designed to collect the LOs features based on ontology, following the metadata standards such as Dublin Core (DCMI 1995) and ALOCoM. The concept of keywords in SLOs focused on the content of science subjects in Thai as defined by the Simple Knowledge Organization System or SKOS (W3C 2009) and the UNESCO Thesaurus (ULCC 2003). Correlated Recommendation Module (CR Module): It is a module for developing the predictive model that is able to recommend suitable LOs. This is generated from

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Fig. 1 Architecture of Learning Objects Management Model (LOMM)

&

the correlation between learning styles and LOs characteristics using decision tree algorithm. Learning Objects Semantic Search Module (LOsS Module): It is a module for searching and ranking the LOs according to the different learning styles of users by using semantic search technique. The LOsS Module is associated with 3 modules (the LS Module, the LOsC Module, and the CR Module). The procedure is explained as follows: First, when a learner logs in or registers with the system, a session is initiated that collects the learner’s profile and learning style at the LS Module. In the second step, when a learner inputs keyword search to the system, a semantic search will be executed, which finds the appropriate LOs from the LOsS Module, and it also retrieves the information about LOs characteristics from the LOsC Module. During the third step, after getting the LOs information, the learning styles of the learner are obtained from the LS Module, and find the correlated learning styles and LOs characteristics of the CR Module. The final step of LOMM is to provide learners with the results, which relate the keywords and ranked LOs recommendation based on individual characteristics.

The sequence diagram of LOMM is given in Fig. 2. When a learner logs in and sends the registration information to the system, a session is initiated to collect the learner’s profile and learning style at the LS module. Thereafter, when a learner inputs keyword search to the system, a semantic search on the LOs from the LOsS module will be initiated. Request for the information about the learning object’s characteristics of the LOsC module will be sent. After getting the LOs information, the LS module looks for matches based on correlation between learning styles and learning objects’ characteristics from the CR module. The final session of LOMM is to provide the results to the learners.

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Fig. 2 The sequence diagram of LOMM

It is reiterated that the aim of this study is to develop a methodology to find the correlation between learning styles and LOs characteristics, in order to provide LOs and ranked LOs based on the different learning styles of the learners and the keywords or topics that the learners are interested in. Specifically, such a model is developed in order to address some of the drawbacks of other methods. For example, the proposed model provides semantic search functions, which are not available in AnnForum model and it also provides personalized search, which did not work effectively in Protus’ model. Furthermore, the proposed model covers more of the contents and characteristics of LOs than Ocepek’s model does (2013).

4 Research procedure In this section, the instrument and methodology were designed for the acquisition of data concerning student profiles, learning styles and features of the LOs. The research procedure consists of three parts: (1) Data Collection, (2) Data Preparation, and (3) Data Analysis and Evaluation. Figure 3 shows the research procedure undertaken in this study, followed by a description of the stages. 4.1 Data collection The experimental data in this research were collected through surveys from high school students in the Nakhon Ratchasima Province, the second most populated province in Thailand. A questionnaire was used to establish the relationship between learner characteristics and features of LOs that the learners preferred. The questionnaire was divided into three parts. The first part aimed to collect the personal and background data of the learners, which include profile information such as gender, age, grade level and favorite science subject of the learners. Since the implementation of the 1992 National Education Plan, the Thai Government has set up a policy to promote education for gifted children, particularly in the fields of science. In order for Thai students to become part of the nations of the world and to prepare them for the twenty-first century, the learners with positive attitudes toward science subject will likely having higher

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achievement (Poisson 2000). Engaging instruction was related to higher science achievement (Martin, Mullis, Foy, and Stanco 2012; Raza and Shah 2011). Therefore, the favorite in science subject was defined in the part of the learner’s profile in this study. The second part included all the items for the determination of the learning modalities of the learner. GRSLSS is used in this research and it is appropriate for high school students as indicated from previous research (Baykul et al. 2010). Although GRSLSS consists of a total of 60 items under six categories: competitive, collaborative, avoidant, participant, dependent and independent (Grasha and Riechmann 1974), results from many research reports in Thailand and other countries using GRSLSS point out that the categorized characteristics of high school students were mostly participant and collaborative. They also preferred competitive while quite a few were categorized as avoidant (Babadogan and Kilic 2012; Dinçol et al. 2011; Maneenil et al. 2010; Thonthai 2009). However, due to the high number of variables, reducing the irrelevant questions and amount of information was necessary in order to avoid the situation whereby the participants could be bored in the process. As a result, the variables in the GRSLSS have been limited to those related to learners’ behaviors only and they mostly fall into three categories: participant, collaborative and independent. The survey therefore consisted of 30 questions only. The instrument was validated to confirm the reliability and validity by tryout with representative samples (121 students). Cronbach’s alpha reliability coefficients were found to be 0.89 for the whole inventory, 0.83 for participant learning styles, 0.78 for collaborative learning styles, and 0.75 for independent learning styles. Therefore, the inventory of GRSLSS in this part can be assumed as reliable. The last part of the instrument is the characteristics of the learning objects that were preferred by the learners. The questions about the LOs features were created and extracted from several parts as follows: the multimedia type features from Metadata Standards (DCMI 1995; IEEE 2002), content object features from ALOCoM ontology (Knight et al. 2005; Verbert et al. 2006), and the environmental features involving the learning procedures from Dunn and Dunn’s learning styles model (Dunn and Dunn 1993). Finally, the questions about the LOs characteristics that the learners preferred consists of 33 items, which were divided into five groups: type of multimedia {Video, Animation, Audio, Presentation, Graphic, Simulation, Link}; type of narrative content {Abstract, Overview, Introduction, Chapter, Summary, Bibliography}; type of cognitive content {Fact, Definiton, Process, Procedure, Demo, Algorithm}; type of

Fig. 3 Flowchart of research procedure

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supporting content {Example, Exercise, Reference, Description, Question, Answer} and type of environment content {Time, Schedule, Sound, Place, Motivation, Conditon, Assignment, Evaluation}. The content validity of the questionnaire in this part was confirmed by Thai education technology professionals prior to the adoption of the instrument in this study. The dataset consists of 1,586 students who studied in high schools in the Nakhon Ratchasima province. The students in each school were randomly selected by sampling from Grade 7 to Grade 12. Figure 3 shows the distribution of learner’s profile and learning styles from the dataset. 4.2 Data preparation In this step, the data from the survey were cleaned and parameters to be used for data analysis was identified. Responses with missing data were ignored in order to ensure the quality and integrity of the information. The data was then reformatted during the data transformation stage and they were prepared in the form of document matrix for storage in the data warehouse. The matrix of transformed and cleaned data was constructed to represent the dataset in this study. The rows of the matrix represented all the attributes as follows: learner’s profile, learning styles, and LOs characteristics. A column of the matrix represents the unique characteristic of a learner. Figure 4 shows the size of the matrix in the data warehouse as (k+i) * N, where k is the number of attributes of the learner’s profile and learning styles, i is the number of attributes of LOs characteristics and N is the number of datasets. After this process, 1,586 records are used as valid samples in the subsequent stages of analysis. In the experiment, the dataset of 1,586 records was randomized and reorganized into 3 datasets as “CaseStudy-1”, “CaseStudy-2” and “CaseStudy-3”. In each randomized dataset, the records were divided into two sets: 70% of the dataset (1,110 records) were

Fig. 4 Distribution of learner’s profile and learning styles of the survey data

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Fig. 5 Matrix of transformed and cleaned data

used to generate the classification model and they are called the “Training Set”. The remaining 30% of the data (476 records) were used for evaluation and testing of the predictive model’s accuracy and they are known as the “Testing Set”. In other words, the three case studies are all based on the same dataset but the records could be used as either training or testing, as shown in Fig. 5. 4.3 Data analysis and evaluation This section describes the process of data analysis and evaluation in order to find the correlation between learning styles and LOs characteristics in the proposed LOMM. The obtained data were analyzed based on featured selection, featured grouping, classification analysis and evaluation model. Figure 3 shows the working procedures that consist of three processes, as follows: 4.3.1 Feature selection and grouping attributes Feature extraction is a useful technique in order to decrease dimensionality and to eliminate noise from the dataset. The Apriori algorithm, an association analysis used extensively in data mining, was used in this stage. It is useful for discovering interesting relationships hidden in large datasets (Tan et al. 2005).

Fig. 6 Steps to develop the classification model

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Fig. 7 Samples of application program developed with Python language

4.3.2 Developing the classification model with training set The predictive model for classification of learners was developed using the training dataset. DT was applied in this process for creating and improving the classification model. The process to develop the classification model consists of four steps as shown in Fig. 6: &

Step 1: To create the classification model for each group of LOs Characteristics using DT.

It has been reported that the use of feature grouping method can improve the classification accuracy of DT (Wanapu et al. 2014a, 2014b). Therefore, DT was used to generate the classification models in this study in which the learner’s profile and learning style (LS Data Set) were used as the input attributes. The groups of LOs characteristics (LOsC Data Set) were used as classifiers. For example, LOs_G1 is based on 4 LOs characteristics of binary features {Video, Animation, Graphic, Simulation}, LOs_G1 is a set of 24 or 16 possible combinations of these characteristics, {YYYY, YYYN, YYNY…etc.}. Depending on the outcomes from the Apriori analysis, the number of LOs characteristics grouping and number of binary features in each group of LOs characteristics

Fig. 8 The result of selected LOs characteristics from Apriori algorithm

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Fig. 9 Grouping of LOs characteristics

were determined based on a minimum support threshold of 0.9 (from a scale ranging from 0 to 1) in the process of feature selection and grouping attributes. &

Step 2: To aggregate the classification model

As the classification models derived in step 1 are based on the same input attributes, they could be aggregated into a single model. The number of rules in each classification model can be calculated by multiplying the number of input attributes in each group, this means CD1*CD2*CD3*…*CDk. C={CD1, CD2,…, CDk} will be a set of the combinations due to each input attribute. In this study, each of these corresponds to the learner’s background and learning style. 24 Learner classes were generated in this study. CD1 ={Participant, Collaborative, Independent} – Learning Style CD2 ={Female, Male} – Gender CD3 ={High, Second}– Grade Level, High refers to grade level 10 to 12 and Second refers to grade level 7 to 9 CD4 ={Favour, Disfavour} – Preference in science subjects as indicated by the student Step 3: To combine the learner’s profile and learning styles into a new classifier The “Learner’s class” or “LC” was a combination of the attributes from the learner’s profile and learning styles. In this study, it is based on the four learner’s background and learning style, {Learning Style, Gender, Grade Level, Science Favorite}. LC is therefore the set of 24 possible combinations of these values, LC={PMSF, PMSM, PMHF,…, IFHM}.

Fig. 10 The top-3 LOs characteristics across groups discovered by Apriori algorithm

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Fig. 11 Results from a comparison between non-grouping and grouping analysis using DT

Step 4: To develop the Classification model The classification model is based on the results from the previous steps. The classification models from the groups of LOs characteristics in Step 2 were used as the input attributes, and the Learner’s class in Step 3 is used as the target attribute for building the classification model. The predictive model for classification of learners is then created by the mapping between the two sets of attributes. A detailed example will be given in the subsequent sections. 4.3.3 Evaluating the predictive modeling (testing set) The evaluation process aimed to evaluate the accuracy and reliability of the prediction model by using the testing dataset. The classification models were used to classify learner’s classes. The assessment of the prediction models yields four possible outcomes, including true positive (TP), false positive (FP), true negative (TN), and false negative (FN). TP represented the number of learner’s class correctly categorized. FP was the number of learner’s class that was not labeled to the particular category, but it should be labeled. FN represented the number of learner’s class incorrectly categorized. And TN was the number of learner’s class that was classified as negative, and it is

Fig. 12 Classification model of LOs_G1 by Decision Tree

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Fig. 13 Classification model of LOs_G2 by Decision Tree

actually categorized as negative. Equation (1) exhibits the calculation formula for the Accuracy (Miao et al. 2009). The programs were developed by Python language based on concept mapping and sample of the code is given in Fig. 7 below. Accuracy ¼

TP þ TN  100% ðTP þ FP þ FN þ TNÞ

ð1Þ

5 Result and discussion This section reports the experimental results according to the proposed method as illustrated in Figs. 1, 3 and 6. The results are divided into three parts: (1) Result from

Fig. 14 Predictive model for classification of learners

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Fig. 15 Predictive model accuracy using 5 versus 8 groups of LOs Characteristics (CaseStudy-1)

feature selection and grouping attributes, (2) Developing the classification model, and (3) Result from the predictive model evaluation. The details are as follows. 5.1 Result from feature selection and grouping The feature selection approach used to reduce the dimensionality space was aimed to eliminate irrelevant or redundant characteristics from the dataset. A minimum support threshold of 0.9 was used in the association rules based on the Apriori algorithm on WEKA. The results identified 19 dominate attributes of LOs characteristics which are: Video, Animation, Graphic, Simulation, Overview, Chapter, Summary, Fact, Process, Procedure, Demo, Example, Exercise, Description, Time, Sound, Place, Motivation and Assignment. This is shown in Fig. 8. Correlation of the LOs characteristics and learning styles was determined by the association rules. The five groups of LOs characteristics are defined as “LOs_G1”, “LOs_G2”, “LOs_G3”, “LOs_G4” and “LOs_G5” as shown in Fig. 9. LOs_G1 is a group of features defined in the metadata standard (IEEE 2002; DCMI 1995). LOs_G2, LOs_G3 and LOs_G4 are the features grouped by ALOCoM ontology (Knight et al. 2005; Verbert et al. 2004). LOs_G5 is a group of features defined in Dunn and Dunn’s learning styles model (1993). In order to improve the performance, it was necessary to investigate the use of combinations of the LOs characteristics using an Apriori algorithm for association rules analysis. The investigation indicated that the dominated relationship of LOs characteristics was corresponded to the results in the study of Wanapu et al. (2014b). The top 3

Fig. 16 Predictive model accuracy using 5 versus 8 groups of LOs Characteristics (CaseStudy-2)

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Fig. 17 Predictive model accuracy using 5 versus 8 groups of LOs Characteristics (CaseStudy-3)

groups were selected and they are shown in Fig. 10. The LOs characteristics in these groups are termed Relationship dominant-1, Relationship dominant-2 and Relationship dominant-3, and the LOs Characteristics are {Summary, Fact and Demo}, {Simulation, Summary and Example} and {Overview, Exercise and Time}, respectively. This indicates that learners who preferred Summary are likely to prefer Fact and Demo. The same applied to the combined group of Relationship dominant-2 and Relationship dominant-3. In the next section, comparative results between 5 groups and 8 groups of LOs characteristics are presented. The LOs characteristics in the eight groups are given below: Group 1 Group 2 Group 3 Group 4 Group 5 Group 6 Group 7 Group 8

is Multimedia Type {Video, Animation, Graphic, Simulation}, is Narrative Content Type {Overview, Chapter, Summary}, is Cognitive Content Type {Fact, Process, Procedure, Demo}, is Supporting Content Type {Example, Exercise, Description}, is Environment Content Type {Time, Schedule, Sound, Place, Assignment}, is Relationship dominant-1 {Summary, Fact, Demo}, is Relationship dominant-2 {Simulation, Summary, Example}. is Relationship dominant-3 {Overview, Exercise, Time}.

5.2 Developing the classification model In this section, the results of the predictive model for classification of learners are presented. The objective is to compare and evaluate the correlation based on 19 non-

Fig. 18 Accuracy comparison of models built from 5 against 8 groups of LOs Characteristics (averaging from all Case Studies)

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grouped characteristics and 8 groups of learning object characteristics. DT was used to perform the analysis of the LS Data Set, which is the classification target, while the LOs characteristics were used as the input attributes. In the analysis of the non-grouped, all the LOs characteristics were used. In the analysis of the grouped, the characteristics in 8 groups were used. The results show that the percentage of correctly classified LS in the grouped attribute analysis was provided better accuracy than the non-grouped attributes as indicated in Fig. 11. The findings suggest that grouping of LOs attributes is more appropriate with better results, and these results are consistent with Wanapu et al. (2014a). Nevertheless, it is noted that the highest accuracy of grouping LOs features was 64.92% and there are rooms to improve. Other classification techniques will be considered in the future in order to improve the results. Figures 12 and 13 illustrate the paths of the association between the input attributes and the classifier for LOs_G1 and LOs_G2. Similar trees have been established for the other 6 groups. Figure 14 illustrates the predictive model relating the LOs characteristics to the Learner classes. The input is an aggregation of the characteristics in 8 groups and the output is one of the 24 sub-models based on learner’s profile and learning styles. 5.3 Result from the predictive model evaluation The predictive models for classification of learners were evaluated using the testing data set of 476 records in the 3 case studies. Analysis results were compared between the 5 and 8 groups of LOs characteristics in each case study. Figures 15, 16 and 17 show that results due to 8 groups of LOs Characteristics have better predictive model and higher accuracy than the results from using 5 groups only in all case studies. This illustrates that the additional associative information in the last groups has enhanced the overall performance.Figure 18 shows that the accuracy value in each sub-model varied between a minimum of 46.2 and 86.1% for 5 groups, and between 59.0 and 100.0% in the case of 8 groups. This may be due to the fact that the number of records in each submodel is different. It is expected that the number of learners in each sub-model in the testing dataset will have impact on the accuracy in each sub-model. For example, in CaseStudy-1, the number of learners in three sub-models {IMSF, IMSD and PMHD} is 12, 9 and 19, respectively. It can be seen that these three sub-models have the lowest accuracy at 46.2, 48.2 and 49.1%, respectively. It means that cautions should be exercised with the results. In particular, the number of training data is likely to be a little bit small in each of the sub-models situation.

6 Conclusion and future work 6.1 Conclusion This paper has described a methodology to find the correlation between learners’ learning styles and LOs characteristics, which forms the basis of a proposed Learning Object Management Model (LOMM) for the matching of ranked LOs to different users. This approach is designed to support the numerous characteristics of LOs and it

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is expected that the proposed approach will be applicable to e-learning systems and digital repositories. In this study, DT and Apriori algorithms were used to generate the predictive model for the classification of the learners. A survey of 1,586 students was carried out and the information on the profile of the students, learning style of each learner based on GRSLSS, and LOs characteristics preferred by the learners were collected. The dataset was randomized and reorganized into 3 datasets as “CaseStudy-1”, “CaseStudy-2” and “CaseStudy-3”. In each randomized dataset, the records were divided into two sets to establish the classification model, and to evaluate or test the model. Thirty three LOs characteristics were analysed in order to find the correlation with the learning style, background of the learners and LOs characteristics. The results of this study showed that 19 of the LOs characteristics that the learners preferred have dominant attributes and 3 combinations across different groups of the LOs characteristics can be effectively discovered by the Apriori algorithm. Most of the LOs characteristics used to generate the predictive models are commonly preferred by the learners and they have correlation with learning styles of the learners. The developed classification model of the learners consists of 24 sub-models, which are used to predict the characteristics of the LOs. The finding also suggested that the prediction based on grouped LOs characteristics had higher accuracy than that of the non-grouped LOs characteristics. Moreover, the accuracy of prediction also depends on the number of groups of LOs used. 6.2 Future work Up to this point, another issue had to be taken into account is the learning achievement of the learners when the LOMM is applied. The relationships between LOs and LS presented in the LOMM should supply LOs to learners appropriately on their demand. Therefore, an approach to investigate this issue needs to be conducted. However, this investigation process is far from a trivial task. The investigation process has to be implemented practically and needs to be performed in the long run. In this current study, to confirm our hypothesis, the proposed models were theoretically evaluated by the cross validation method. The evaluation showed the promising results, which is a good commitment to the next step. The future work of this study is to develop a semantic search web-based application constructed on LOMM. The application will provide LOs and ranked LOs based on different LS of the learners, and keywords or topics that learners are interested in. Once the application has been developed, it will be used to assess the effectiveness of the LOMM. In order to perform the assessment, under a certain setting, students (test subjects) will be divided into two groups, experimental group and control group. The experimental group will be supplied with the developed application, whereas the control group will be supplied with a generic search tool. The effectiveness of LOMM model will be assessed by a group of qualitative indices, for examples, improvement of study or satisfaction of search results. The outcomes of this assessment will be an evidence that the relationship between LOs and LS presented in the LOMM can enhance learning achievement of the learners.

Educ Inf Technol Acknowledgments I would like to thank the Office of the Higher Education Commission, Thailand for providing the grant under the Higher Education Research Promotion and National Research University. I would also like to express my gratitude to Assistant Professor Dr. Issra Pramoolsook at the School of Foreign Languages, Suranaree University of Technology, for his assistance in correcting grammatical errors and English usage throughout this research paper.

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