Cognitive Based Learning Environment and Content Description: An Ontological Approach

— The huge amount of digital educational resources availability and extensive integration of information technology in e-learning domain, have initiated changes in educational paradigm. The higher educational institutions are with a great challenge in how to prepare their students towards cognitive thinking, to solve realistic problems and to meet the demands of the knowledge society. In this connection, In the existing educational metadata standards, there is an apparent lack of metadata elements from psychological and cognitive science perspective to satisfy the needs of e-learner. The assignment of pedagogical metadata that characterizes the learning resources as per the cognitive prerequisites of a learner, is an important step towards delivering contents of a domain. In this work, first we explore some core issues and the implications for the success of web-based distance learning environments. Then, we proposed the cognitive principles based descriptive metadata for the course-based learning environment. In order to make the proposed metadata schema to support the recent Educational Semantic-web Vision, we define the ontological structure of course-contents. Finally, the article ends with an investigation on the assessment of learner’s cognitive learning skills through using ANCOVA statistical evaluation approach.

content modeling based on cognitive principles. Finally, investigated the practical aspect of proposed ontologybased content model, through considering course based learning environment as use case scenario.

II. ISSUES AND IMPLICATIONS
In learner centered web-based learning environments, the learner is the central figure of the educational process. E-learning provides number of benefits that facilitate the development of high-level cognitive skills [10]. But in designing e-Learning courses there are some restrictive factors such as interaction support and presentation, to make it as an alternative to face-to-face teaching at universities. In connection to this, the effective use of online courses also depends on learner's cognitive certainty

A. Design Principles and Interaction Support
The principal task to be supported by an e-learning environment is that of learning and instruction support. In order to encourage higher-order thinking skills of learner, the learning environment needs to support the cognition-oriented metadata attributes such as Bloom's taxonomy. In advanced learning environments, the appropriate instructional procedures encourage the learners to engage in the conscious learning process. The principles of instructional design should be intended to assist in the presentation of information so as to decrease learner's cognitive load and encourages learner towards cognitive learning activities. Through providing an interaction support between the learner and learning environment, it can help the learner to involve in various learning activities related to a subject domain.

B. Content Description and Presentation
In an e-learning environment, the content presentation and instructional approach are expected to produce a desirable learning effect in an e-learner. As mentioned by Dicheva and Dichev [11] the students are often unaware of the context of the learning task when they start to learn new concepts. The content presentation approach needs to make the learner, to learn and interpret the correlated and in-depth concepts of learning domain so that the learner gets exposed to higher order thinking skills. The ontology based content presentation will support learning process in two ways. First, the conceptual structure will be conveyed to the learner, Secondly the embedded semantic knowledge will be expressed in a natural human narrative form to achieve the effective learning.

C. Cognitive Certainty of Learner
Uncertainty is a natural user experience in the process of information seeking and acquiring meaning [12] while travelling beyond what is known to the user. Cognitive learner desire to know from what they already know and wishes to learn more about a particular subject area to increase their knowledge. Cognition is the process of thinking towards achieving the particular solution to the problem or seeking clarity from ambiguous approaches, which needs exploratory or investigative searching of supportive information.
D. Cognitive certainty paves the way to obtain the following  Familiarity with the domain of their goal  Process the ways to achieve their goals  Clarity in explaining his learning and put the learning to use.  Clarity in between what they know and want to know.  Ability to tackle complex problems through dividing into manageable smaller tasks

III. COGNITIVE PROCESS AND CONTENT DESCRIPTION
From the educational domain point of view, in existing metadata standards there is an apparent lack of elements from the psychological perspective to satisfy all the needs of e-learner. When performing the search for any particular topic, the smart learners are frequently looking for additional facts from the context of search topic. So that this work primarily revolves on cognitive-based descriptions of content. In this direction, the proposed metadata can be used to improve the search process, to retrieve additional topics which are related contextually and pedagogically with search topic.
The term cognitive style of learning deals with the learner's activities such as thinking, perceiving, remembering, etc. but not directly with the learning content [13]. But the learner's cognitive activity can be influenced by means of modeling and presentation of supplementary (pedagogical) material of course content. The content presentation environment helps in learning style, which refers to how a learner perceives, interacts with, and responds to the learning environment [14]. The supplementary material for learning environments can be basically identified into different classes, according to Bloom's Taxonomy [7]. The learner's learning skills can be divided into Understanding, Comprehension, Applying, Analyzing, and Evaluating. The spectrum of tasks provided Bloom's Taxonomy are mapped with concerned supportive material type and their Impact on learning process, that are as shown in Table 1. These material types (metadata) enables in the process of building the ontology for e-learning systems to effectively locate learning materials to match learner needs. It provides explanations or investigation by looking at the macro level on the overall functionality of the system in relationship with the environment to develop divergent conclusions.
It gives guidelines for decision making or judgment based on a relevant set of strategies with a given purpose.

IV. ONTOLOGICAL FRAMEWORK
In this section, we present the ontological framework of course content model to support cognitive skill development process. That needs shared understanding between terms of various metadata vocabularies. In order to meet the needs of cognitive based course content modeling, we categorize the course contents basically into two types, as core concepts and additional supportive resources. Where, the core concepts are the educational resources that constitute as the backbone of student's learning activity and the supportive resources corresponds to the reference material to complement the core concept.
In order to provide an explicit specification of the conceptual model, based on Table 1 in the previous section, we define an ontological approach. That represents core concepts accompanied with the five categories of supportive resources on the basis of cognitive dimensions such as Explanation, Interactivity, Applicability, Evaluation, and History. As per the cognitive needs of e-learner, this representation is useful to identify learner's intended topic along with the related learning material.
The class Course is used to describe the contents of the course. The Course class and Concept class contains data type properties such as: hasName, hasObjective and hasDescription. The name property is to identify the concept and objective property indicates the aim or purpose of course what we are supposed to learn. In the following, we described the core classes and properties of the ontology shown in Fig 1. The ontology was implemented using Protege [15]. 2) Interactivity: It offers some kind of interactive aspects, which helps the learner to develop a skill or ability related to a concept, through verifying the acquired knowledge by solving the issues, exercises, and real-world problems.
3) Application: Learner is able to develop knowledge on the applicability of concept through understanding various properties, behavior and context of application to real-life situations and problems. 4) Evaluation: this core class contains the information related implementation and applicability of concept so that learner is able to understand what works and what does not work in any particular context or application of the concept. The evidence and demonstration illustrate on how particular task can be performed. 5) History: It provides guidance to the learner on, what are the prerequisite and related materials of current learning concept, it improves the relational and comparative skills of a learner.

V. SYSTEM IMPLEMENTATION AND ANALYSIS
Most of the existing course based and adaptive learning environments deliver learning materials based on learner's preferences and context but not construing about what extent the material can influence the learner's cognitive skills and investigative thinking. Learner may have different viewpoints, cognitive skills, and knowledge levels of learning materials [16] and It is important to consider cognitive learning styles while designing the web-based courses [17].
The distinct feature of the proposed system is, when the learner navigates to a particular concept, the learner is able to view other topic titles that are related to current learning topic which improves the learner's understandability. To evaluate the proposed approaches as a proof of concept, the researcher has implemented a web-based prototype in the research center of computer science department in his university. The experiment is performed in an intranet enabled environment using local university database as dataset for learning materials.
Here, the experimental results of the proposed web-based prototype that is being implemented in our university are presented. Fig 2, illustrates the course content presentation approach consists of base concept and other semantically related concepts that can improve the learner performance and the student has the option to freely navigate using any of the onscreen links of the relevant concepts. The purpose of the experiment is to evaluate the performance of the proposed e-learning system through utilizing statistical approach, pre-test and post-test experimental design. The researcher preferred to test the difference of achievements between the experimental group and the control group using one-way Analysis of Covariance (ANCOVA) as it makes the group comparisons more fair [18].
In this study, the aim was to determine whether the proposed system meet most learners' requirements to promote their learning performance. To perform this test the course experts identified learning concepts on "Data Structures" subject for the students, to whom the subject is in their curriculum and then designed a corresponding test item for each learning concept. The students were asked to complete a pretest with 20 test items; after completing all course units, the learners were asked to take another 20-item post-test to evaluate their learning achievements.

A. The experimental procedure comprised the following phases
Groups: A total of 24 undergraduate students were enrolled to participate in the test exam; the students were equally divided into experimental (treatment) group and control group. The control group students were allowed to browse and learn the concepts through the traditional approach. The experimental group was allowed to study the stipulated concepts through using the proposed system.

B. Pre-test
Through the pre-test, the system could obtain information about learners' prior knowledge levels of the learning concepts as well as the basis of comparison for post-test results.

C. Post-test
Learners were allowed for taking post-test when they finished their learning processes and the test results were aimed to compare the learning performance of the two groups of students after taking the course with different learning environment approaches.

D. Data analysis
This study analyzed and estimated the performance of system through the Analysis of Covariance between the experimental group and the control group.
The difference in the experimental group's score from the pre-test to the post-test indicated the change in the value of the dependent variable that could be expected to occur with exposure to the treatment. The descriptive statistics for the analysis is shown in Table 2, whereas Table 3 presents a summary result of the ANCOVA analysis on the overall post-practice achievement test.
The analysis of covariance results (F=4.77, P=0.038) indicates that the experimental group scored significantly higher than the control group in the post-test and we can acknowledge the same from Table 2. Table 3 shows that the research hypothesis holds well with significant level α=0.05 and P = 0.038552< 0.05. This means that the proposed learning model provides benefits in terms of learning performance promotion.

VI. CONCLUSION
The goal of this work is to come up with ontology with a limited set of classes that describes a learning resource, where each class of the ontology stands for a particular instructional or pedagogical role of learning resource. We believe that the proposed resource description metadata based on cognitive flexibility can help students in advanced knowledge acquisition and it should encourage learners to explore new knowledge in various concrete situations. Here, we dealt with the investigation of cognitive style of learning and ontology