DESIGNING AN EXPERT SYSTEM BASED E -COURSE IN PHYSICS … · Alajab Ismail, Designing an Expert...

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International Journal of Information Technology & Management Information System (IJITMIS) Volume 8, Issue 1, January-April 2017, pp.01–21, Article ID: IJITMIS_08_01_001 Available online at http://www.iaeme.com/ IJITMIS/issues.asp?JType= IJITMIS&VType=8&IType=1 Journal Impact Factor (2016): 6.9081 (Calculated by GISI) www.jifactor.com ISSN Print: 0976 – 6405 and ISSN Online: 0976 – 6413 © IAEME Publication

DESIGNING AN EXPERT SYSTEM BASED E-COURSE IN PHYSICS AND ASSESSING ITS EFFECTIVENESS

ON DEVELOPING BAHRAINI SECONDARY SCHOOL STUDENTS' COGNITIVE ACHIEVEMENT AND

SCIENTIFIC THINKING SKILLS

Dr. Abdelaziz Mohamed Gouda Abdelaziz Salama Arabian Gulf University, Kingdom of Bahrain

Dr. Alajab Mohammed Alajab Ismail Arabian Gulf University, Kingdom of Bahrain

ABSTRACT Using expert systems and artificial intelligence in designing e-Learning and its related

technologies is formulating a sound trend in research and development in the field of educational technology. However, adopting such new innovations depends on developmental research findings that are to be carried on different learning tasks, academic fields, learner variables, and contexts. The current research aims at designing an Expert Systems Based e-Course in Secondary School Physics and discovering its effectiveness in developing Bahraini students’ cognitive achievement and scientific thinking skills as compared to designing the same e-Course without expert systems. The contention of this research is that designing e-Learning based on using expert systems can enhance scientific thinking skills as well as achievement. Due to the developmental nature of this research, the researchers employed the Developmental Research Method as defined by Elgazzar (2014) with two experimental groups’ pre/post test Quasi-experimental design, six research hypotheses were formulated. So, the content of Physics (102) course was analyzed and a list of design standards was derived and both lists were refereed. The first researcher developed two designs of e-Courses of two units from the Physics (102) course: one was based on Expert Systems and the second without expert systems using Khamis (2007) ISD model and both designs were refereed to meet the derived list of design standards. A cognitive achievement test and scientific thinking skills test were developed and proven to be valid and reliable. The research sample was made up of (50) students from two classes drawn from two male secondary schools, Kingdom of Bahrain, (25) students in each class (cluster). These two classes were assigned randomly to the two experimental groups in the design: the first group was taught by using expert system based e-Course design, and the second group was taught by using the e-Course without expert system based design. The research experiment was carried on the 2014/2015 academic year and the two research tools were administered pre/post the experimentation. Appropriate statistical procedures were applied in testing the six research hypotheses. Results of testing those hypotheses revealed superiority of the expert systems based e-Course design of Physics as compared to the same e-

Designing an Expert System Based E-Course in Physics and Assessing its Effectiveness on Developing Bahraini Secondary School Students' Cognitive Achievement and Scientific Thinking Skills


Course without expert systems design in both achievement and scientific thinking skills in post-tests and McGugian modified gain ratios. The paper also contains a list of relevant recommendations, a list of future fellow up researches, Figures, Tables, and References. Key words: Expert systems e-Course Design, expert systems e-Learning design, effectiveness, cognitive achievement, scientific thinking skills in science, developmental research method, Physics education, secondary school students.

Cite this Article: Dr. Abdelaziz Mohamed Gouda Abdelaziz Salama and Dr. Alajab Mohammed Alajab Ismail, Designing an Expert System Based E-Course in Physics and Assessing its Effectiveness on Developing Bahraini Secondary School Students' Cognitive Achievement and Scientific Thinking Skills. International Journal of Information Technology & Management Information System 8(1), 2017, pp. 01–21. http://www.iaeme.com/IJITMIS/issues.asp?JType=IJITMIS&VType=8&IType=1

1. INTRODUCATION Physics education refers to both the methods currently used to teach physics and to an area of pedagogical research that seeks to improve the outcomes of those methods. Historically, physics has been taught at high school and college level primarily by the lecture method together with laboratory exercises to verify concepts taught in the lectures. Many problems facing teaching secondary school physics as well as less interest in physics are contributed not only to students but also to teachers.

Many former physics students remember physics as their worst school subjects (knight, 2004), and nearly always these memories include images of a lecture and associated experiments in a laboratory. Aeons (1977) mentioned that research is showing that didactic exposition of abstract ideas and lines of reasoning (however engaging and we might try to make them clear) to passive listeners yields pathetically thin results in learning and understanding except in the very small percentage of students who are specially gifted in the field.

Teodorescu, Bennhold,Feldman and Medsker (2013) described research on a classification of physics problems in the context of introductory physics courses. Their classification, called the Taxonomy of Introductory Physics Problems (TIPP), relates physics problems to the cognitive processes required to solve them. TIPP was created in order to design educational objectives, to develop assessments that can evaluate individual component processes of the physics problem-solving process, and to guide curriculum design in introductory physics courses, specifically within the context of a "thinking-skills" curriculum. Moreover, TIPP enables future physics education researchers to investigate to what extent the cognitive processes presented in various taxonomies of educational objectives are exercised during physics problem solving and what relationship might exist between such processes. They described the taxonomy, gave examples of classifications of physics problems, and discussed the validity and reliability of this tool,

Shekarbaghani (2016) conducted a qualitative study, which was done in 2013-2014, to compare physics curriculum elements of Iran with the countries studied. Countries studied: Singapore, Turkey, India, England and Australia have diverse educational system. In his study, the structure of the educational system, the physics curriculum, teaching methods, students' achievement evaluation methods were studied and compared. The aim of the research was to identify the features of the physics curriculum in Iran. In some cases, similarities and differences were observed. From the major problems in the physics curriculum of Iran is many number of books is more than in other countries. And time teaching physics Iranian schools is less than in other countries, While the content of physics books of Iranian schools from all countries studied is further and to do physics experiments in Iranian schools are of less importance. Iran evaluation system is the traditional way and held for final evaluation. The results can help the educational planners and authors of physics textbooks perform more accurate and more comprehensive correction to the physics curriculum.

Dr. Abdelaziz Mohamed Gouda Abdelaziz Salama and Dr. Alajab Mohammed Alajab Ismail


In their topic “who’s teaching what in high school physics? White and Tyler (2015) from USA told that: approximately 27,000 teachers taught at least one physics course in a U.S. high school during the school year 2012-13, and about one-third of those teachers have earned a degree in physics or physics education; the vast majority of the others have earned degrees in a variety of other science fields. Additional more; about 53,000 physics classes were taught, ranging from conceptual physics through AP and second-year physics classes. Using data from the 2012-13 Nationwide Survey of High School Physics Teachers, they look at the teaching experience and academic preparation of teachers teaching various physics classes. They see that teachers with a degree in physics or physics education teach mostly physics classes (as opposed to non-physics). Furthermore, teachers with physics teaching experience and a degree in physics or physics education teach more AP and Honors physics classes than any other group of teachers.

In Nigeria, physics instruction in the secondary schools is a fundamental panacea towards achieving scientific knowledgeable citizens which can propel a nation in the realization of a sustainable economic force. Aderonmu and Obafemi (2015) explored physics instruction in Nigerian secondary schools and the way forward for the attainment of global competitiveness. The research has descriptive survey design. Ninety-two (92) physics teachers and eight secondary schools (four in each Local Government Area) were selected using a purposive sampling technique for the study. The research instruments used for the study were "Questionnaire on Ordeal in Physics Instruction in Secondary School (QOPISS) and Physics Practical Apparatus Checklist (PPAC).The data were analyzed according to research questions using the frequency count, percentage, mean, standard deviation and ranking. The study revealed that qualified physics teachers are not adequate for proper teaching of physics, laboratory apparatus are insufficient for effective practical activities in physics teaching and learning in both rural and urban schools, the lecture and problem solving methods are the most applied instructional strategy employed during physics instruction and physics teachers do not utilize ICT tools in teaching physics. Based on the findings of the study, it was recommended that qualified physics teachers should be employed in the secondary schools, all physics laboratories both in the urban and rural secondary schools should be well equipped by relevant authorities and stake holders, appropriate teaching methodologies and ICT tools integration in the teaching and learning of physics should be employed by physics teachers during physics instruction.

Improving high school physics teaching and learning is important to the long-term success of science, technology, engineering, and mathematics (STEM) education. Efforts are currently in place to develop an understanding of science among high school students through formal and informal educational experiences in engineering design activities emphasizing the science and engineering practices included in the Next Generation Science Standards (NGSS) framework (NGSS Lead States, 2013). Huang, Mejia, Becker and Neilson (2015) investigate physics learning and teaching research and the use of engineering design in the teaching of physics. By integrating engineering into STEM, students may apply scientific ideas to solving an engineering design problem while carrying and transferring knowledge in core science areas. The purpose of the research was to investigate perceptions of physics teachers at high schools across the United States.

The role of analogies as tools for teaching difficult science concepts has been widely discussed in science education. The application of analogies in the context of sustainable education involves richer potential. The purposeful use of appropriate analogies can facilitate analogical thinking and transfer skills, as well as develop abilities which are required for life and lifelong learning, including successful integration into modern society and facility within our technology saturated world. Analogical thinking supports development of students' higher order thinking skills. Jonane (2015) conducted a study to identify Latvian physics teachers' views on the importance of analogies and the methodology of their usage in physics education, as well as to discover innovative examples of analogies. The study involves both quantitative and qualitative methodology: survey of 35 secondary school physics teachers and group interviews with 18 experienced physics teachers. The findings revealed that, in general, now and then Latvian physics teachers use analogies in their pedagogical practice, although they are mostly simplistic



and with illustrative character. Some teachers use analogies in order to help students build new knowledge through activating, transferring, and applying existing knowledge and skills in unfamiliar situations.

To improve physics teaching outcomes, Meli, Zacharos and Koliopoulos from Greece (2016) present a case study that examines the level of integration of mathematical knowledge in physics problem solving among first grade students of upper secondary school. We explore the ways in which two specific students utilize their knowledge and we attempt to identify the epistemological framings they refer to while solving a physics problem. Participant observation was used for data collection, and the students' verbal interactions were video-recorded. The analysis shows that they tend to use a wide spectrum of epistemological framings that entangle mathematics and physics but at the same time face significant practical difficulties in modulating the two subjects.

E-learning in teaching and learning physics considered as the easy way to use information and communication technologies by using of the internet. With the support of e-learning secondary as well as higher education can be delivered anywhere and at any time. Yap and Chew (2013) from Singapore, reported the effectiveness of demonstrations supported by appropriate information and communication technology (ICT) tools such as dataloggers, animations and video clips on upper secondary school students' attitudes towards the learning of physics. A sample of 94 secondary four expressed stream (age 16 years) and secondary five normal stream (age 17 years) physics students from four physics classes of a secondary school in Singapore was selected to participate in the study. A pretest-posttest quantitative experimental design was used. The results indicated that, for both the express and normal streams, attitudes towards the learning of physics improved significantly with the use of demonstrations supported by the appropriate use of ICT tools. Although e-Learning is very importance in Libyan higher education, its implementation is facing many challenges in the Libyan universities. A paper by Aisha Ammar and Rowad Adel (2015) focused on the implementation of e-learning in Libyan Universities. The main aim of their paper was to discover the implementation of e-learning in Libyan higher education and to identify the factors affecting the use of its technology. The research findings and recommendations will benefit the Libyan policymakers and the stakeholders.

Akinbobola (2015) assessed the enhancement of transfer of knowledge in physics through the use of effective teaching strategies in Nigerian senior secondary schools. Non-randomized pretest-posttest control group design was adopted for the study. A total of 278 physics students took part in the study. Transfer of Knowledge Test in Physics (TKTP) with the internal consistency of 0.76 using Kuder Richardson formula 21 was the instrument used in collecting data. Analysis of Covariance (ANCOVA) and t-test were used to analyze the data. The results showed that guided discovery was the most effective in facilitating students' transfer of knowledge in physics. This was followed by demonstration while expository was found to be the least effective. Also, no significant difference exists in the transfer of knowledge of male and female physics students taught with guided discovery, demonstration and expository teaching strategies. It is recommended that guided discovery and other student-centered teaching strategies should be adopted for teaching various concepts in physics so as to engage the students in various activities for meaningful acquisition and transfer of scientific knowledge processes and ethics. Also, physics teacher must emphasize on a variety of procedures for promoting insight, meaningfulness, organization of experience, discovery of interrelatedness among ideas and techniques, and the application of knowledge acquired in one situation to a variety of situations.

Although eLearning is the use of technology for teaching, learning and assessment, there is no common approach to it across the South African Higher Education Institutions. There is, therefore, a concern that the full potential of eLearning approach is not utilized. Bagarukayo and Kalema examined the nature and the extent of eLearning activities in the South African (SA) universities. The research method employed in this paper was based on literature review; sources from the last decade include journals, conferences, books and websites. The findings showed that the level of eLearning usage and adoption varies in different universities due to several challenges such as those of technology and institutions. We give an overview of studies conducted in eLearning in SA universities, highlighting challenges and the best practices. They



recommend management involvement of faculties in policy decisions and investment in technological innovations to address these challenges issues.

Tuul, Banzragch and Saizmaa (2016) reviewed the e-learning course development in selected universities of Mongolia and attempt to classify the e-learning programs that are in practice at the tertiary education level in the country. The paper used both secondary and primary sources. The authors determined what factors influence e-learning type classification and how time consuming is e-learning in course development stage in comparison to that of face-to-face learning? Methods such as computation using threshold values, "k"-means clustering, and comparison of means using paired "t" tests were used. Furthermore, comparison of means was used to validate the factors. In conclusion, authors delivered recommendations based on analysis lessons learned for further development. This research had practical implications for higher education managers to make informed decisions.

Milner-Bolotin (2016) discussed how modern technology, such as electronic response systems, PeerWise system, data collection and analysis tools, computer simulations, and modeling software can be used in physics courses to promote teacher-candidates' professional competencies and their positive attitudes about mathematics and science education. The study showed how modeling technology-enhanced deliberate pedagogical thinking in physics methods courses can improve teacher-candidates' subject-specific pedagogical knowledge and their positive attitudes about science learning. The study also discusses the potential challenges that must be addressed in order to help teacher-candidates successfully implement these pedagogies during the practicum and in their early years of teaching.

The goal of the present study is to design an expert system based on e-Course in physics and assesses its effectiveness on developing Bahraini secondary school students' cognitive achievement in physics as well as their scientific thinking skills.

2. DEVELOPING EXPERT SYSTEMS BASED ON E-COURSE FOR TEACHING SECONDARY SCHOOL PHYSICS Expert systems were first introduced by the Stanford Heuristic Programming Project led by Edward Feigenbaum, who is sometimes termed the "father of expert systems"; other key early contributors were Jairus Lainibo, Bruce Buchanan, and Randall Davis. The Stanford researchers tried to identify domains where expertise was highly valued and complex, such as diagnosing infectious diseases (Mycin) and identifying unknown organic molecules (Dendral). Although that "intelligent systems derive their power from the knowledge they possess rather than from the specific formalisms and inference schemes they use (Edward, 1977).

An expert system is a computer program that uses artificial intelligence technologies to simulate the judgment and behavior of a human or an organization that has expert knowledge and experience in a particular field. In artificial intelligence, an expert system is a computer system that emulates the decision-making ability of a human expert (Jackson, 1998). One example of an expert system is an artificial intelligence system that emulates an auto mechanic's knowledge in diagnosing automobile problems. This hypothetical expert system would likely be the result of engineering using an actual mechanic's knowledge base.

An expert system is divided into two subsystems: the inference engine and the knowledge (Nwigbo & Agbo, 2010).The knowledge base represent facts and rules. The inference engine applies the rules to the known facts to deduce new facts. Inference engines can also include explanation and debugging abilities.

The goal of knowledge-based expert systems is to make the critical information required for the system to work explicitly rather than implicitly (Hayes-Roth, Waterman and Lenat, 1983). In a traditional computer program the logic is embedded in code that can typically only be reviewed by an IT specialist. With an expert system, the goal was to specify the rules in a format that was intuitive and easily understood, reviewed, and even edited by domain experts rather than IT experts. The benefits of this explicit knowledge representation were rapid development and ease of maintenance.



2.1. Components of an Expert System An expert system has 3 components:

The Knowledge Base - Where the information is stored in the expert system in the form of facts and rules (basically a series of IF statements). This is where the programmer writes the code for the expert system.

The User Interface - Where the user interacts with the expert system. In other words where questions are asked, and advice is produced.

Inference Engine - This applies the facts to the rules and determines the questions to be asked by the user in the user interface and in which order they will be asked. This is the 'invisible' part of the expert system, which is active during a consolation of the system (when the user chooses to run the program).

Figure 1 shows educational expert system components.

Hayes-Rot (1983) divides expert systems applications into 10 categories as illustrated in the following Table

Table 1 Categories of expert systems applications

Category Problem addressed Examples Interpretation Inferring situation descriptions from

sensor data Hearsay (speech recognition), PROSPECTOR

Prediction Inferring likely consequences of given situations

Preterm Birth Risk Assessment

Diagnosis Inferring system malfunctions from observables

CADUCEUS, MYCIN, PUFF, Mistral, Eydenet, Kaleidos

Design Configuring objects under constraints Dendral, Mortgage Loan Advisor, R1 (DEC VAX Configuration)

Planning Designing actions Mission Planning for Autonomous Underwater Vehicle

Monitoring Comparing observations to plan REACTOR

Knowledge

Base

Interface

Engine

User

Interface

Human

Expert

Knowledge

Engineer

User



vulnerabilities Debugging Providing incremental solutions for

complex problems SAINT, MATHLAB, MACSYMA

Repair Executing a plan to administer a prescribed remedy

Toxic Spill Crisis Management

Instruction Diagnosing, assessing, and repairing student behavior

SMH.PAL, Intelligent Clinical Training, STEAMER

Control Interpreting, predicting, repairing, and monitoring system behaviors

Real Time Process Control, Space Shuttle Mission Control

From a historical point of view, computers have been employed within the field of education for many years, often with disappointing results. However, recent and current research within the field of artificial intelligence is having a positive impact on educational applications. For example, there now exists ICAI (intelligent computer-assisted instruction) systems to teach or tutor many different subjects; a number of such systems are discussed herein. In addition to CAI (computer-assisted instruction) systems, we discuss the development of learning environments that are designed to facilitate student-initiated learning. A third major application is the use of expert systems to assist with educational diagnosis and assessment. During the course of our discussion of these three major application areas, we indicate where AI has already played a major role in the development of such systems and where further research is required in order to overcome current limitations (Marlene, 1985).

2.2. Using Khamis (2007) ID Model for for Developing an Expert System for Teaching Secondary School Physics in Bahrain The development of the proposed expert system and the e-Course for teaching physics was implemented according to Khamis (2007) instructional design model. Instructional design models help instructional designers make sense of abstract learning theory and enable real world application. Khamis (2007) instruction design model is a four phase process. Figure 2 shows Khamis (1987) instructional design model phases and components.

2.2.1. Phase I: Analysis Phase Analysis phase consists of determining the program’s objectives. It also requires an instructional designer to create reasonable goals for his project. The goal behind this work was to design an expert system based on e-course in physics and assess its effectiveness in developing Bahraini secondary school students' cognitive achievement and scientific thinking skills.

During this phase, the designer / system developer also need to make an assessment, particularly on the characteristics of the target group to determine the following:

Analyze of the problem and assessing the needs Choose the appropriate solutions and appropriate programs. Analyze tasks and content. Analyze learners’ characteristics and their entry behavior. Analyze cost-benefit. Analysis of resources and constrains. The activities of this phase are to ensure that the design process has a guidance and direction that can

be followed. In this phase, also the various elements, whether in terms of content, screen design, system exploration and will be determined as well.

The proposed expert system for teaching physics was developed for Bahraini first year secondary school students. Candidates possess the required scientific knowledge in science and physics that help them interact with the system; also, they possess the technical knowledge. The desired behaviours change was stated as learning objectives of the units, and instructional design standers for developing the e-Course



for the proposed virtual learning environment were generated: analyze the target advance (the students who study secondary school physics at .), identify the participates instructional need for the e-Course in physics and the virtual learning environment to support their physics instruction and analyze the available digital learning recourse and learning objects, learning management system, suggest the needed modification and propose the newly designed content and learning resources.

2.2.2. Phase II: Design This phase centers on creating a way to achieve the goals determined by the instructional designer or system developer. Khamis (2007) ID model proposed the following activities:

Design instructional goals. Design of measurement tools criterion referenced test. Design learning content. Design teaching and learning strategies. Design interactions and control strategies. Design assistance and guidance. Design general instructional strategy. Select multimedia. Define media specifications and standards. Design maps tracks. Design boards events and interfaces interactions. Design scenarios During this phase, the expert system as well as the accompanied e-Course in physics was designed. The

design activities include driving the course instructional objectives (based on needs), analysis of IO and sequencing their instructional hierarchy, identifying the course content, elements and grouped in units, building the criterion-referenced test/tests (CRT) for each unit (pre and post tests), design learning experiences, learners grouping method, and learner instructions, and role of teacher/guide for each objective, choosing the elements of multimedia/learning objects for each objective, and make final selections, designing message and storyboards for the selected media/learning objects for production, designing the expert system navigation techniques, program instructional control, and learner interface, designing the instructional events (Gagne, 1983) and elements of the learning process and building learning/teaching strategy, learner’s interactions with the proposed expert system, external media, and facilities, and requirements depending on the environment of implementation or (VLE).

2.2.3. Phase III: Development The third phase of the model is the development. During the development phase, several things need to be taken into account apart from knowing that teaching methods should be used in CAL. This is because all the key elements that have been created in the previous phase will be translated into a more practical, with the help authoring system or programming language. Here is some software that will be used in developing the website as well as computer specifications to be used in developing the courseware.

Khamis (2007) proposed the following activities: Plan and propagate for production. Have digital media, and plan for new product. Coding the program. Media assembly and directed the initial version of the program. Formative assessment initial version. Modifying the initial version and the final output of the program.



Registration of property rights. Preparation of use and material assistance required evidence. During this phase, content is written and graphics, audio, and photography are also produced and

assembled. In the present study, this phase composed of accessing/obtaining available learning objects, resources, and preparing facilities, modifying/producing learning objects, multimedia elements using production tools and facilitates, digitization and storing multimedia elements and program authoring by using authoring system, set program strategy added external production and media production, and facility preparation for use.

2.2.4. Phase IV: Evaluation However, this is not the only time for revision. As one can see on the figure above, evaluation and revision is a constant phenomenon. One difference between this model and many other models is that this one requires an evaluation following each phase. Furthermore, after the fourth phase of the model, there is another chance to revise your instructional software. This makes the model easy to use. Therefore, any designer or system developer with some knowledge can easily use the Khamis (2007) ID Model.

During the evaluation phase, the instructional designer determines what success will look like and how it will be measured. In general, the evaluation consists of the following components:

Determining the appropriate experimental design of the research. Preparing the program and its accessories and measuring instruments. Application instructions and tribal instruments. Workout program in real situations. Dimensional application of the tools. Monitoring results and processed statistically. Analysis and discussion of results and interpretation.

2.3. Formative Evaluation and Feed Back Continuous corrections, improvement and revision will take place all through the model. During stage of the model data collected from all types of evaluation should be considered i.e. formative and summative. Formative evaluation is iterative and is done throughout the design and development processes. This occurs all throughout Khamis (2007). Summative evaluation consists of tests that are done after the learning materials are delivered. The results from these test help to inform the instructional designer and stake holders on whether or not the learning accomplished its original goals outlined in the analysis phase.

In this phase, the study conducted the following activities: small group or individuals for the formative evaluation of multimedia program or VLE, an extended summative/final evaluation.



Figure 2 Mohammed Khamis Developmental Model (2007)

Phase I: Analysis Phase

1.Analysis of problems and needs assessment. 2. Choose the appropriate solutions and appropriate programs. 3. Analyze tasks and content. 4. Analyze learners’ characteristics and their entry behavior. 5-cost-benefit analysis. 6. Analysis of resources and constrains.

Phase II: Design

1. Design instructional goals. 2. Design of measurement tools criterion referenced test. 3. Design content .4. Design teaching and learning strategies. 5. Design interaction and control strategies. 6. Design assistance and guidance. 7. Design general instructional strategy 8. Select multimedia. 9-define media specifications and standards. 10. Design maps tracks. 11. Design boards events and interfaces interactions. 12. Design scenarios

Phase III: Development

1. Plane and propagate for production. 2- Have digital media, and plan for new product. 3. Coding of the program. 4. Media assembly and directed the initial version of the program. 5. Formative assessment initial version. 6. Modifying the initial version and the final output of the program.7. Registration of property rights. 8. Preparation of use and material assistance required evidence.

Phase IV: Final Evaluation

1. Determine the appropriate experimental design 2. Prepare the program and its accessories and measuring instruments. 3. Application instructions and tribal instruments. 4. Workout program in real situations. 5. Dimensional application of the tools. 6. Monitoring results and processed statistically. 7. Analysis and discussion of results and interpretation.

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2.4. Expert System and Physics Teaching Outcomes Much of the research with the use of expert systems has focused on teachers and students as users of predefined rule bases. Trollip and Lippert (1987) found that the analysis of subject matter required to develop expert systems is so deep and so incisive that learners develop a greater comprehension of their subject matter. They reported that building expert system rule bases engages learners in analytical reasoning, elaboration strategies such as synthesis, and met cognition.

Developing expertise in physics problem solving requires the ability to use mathematics effectively in physical scenarios. Novices and experts often perceive the use of mathematics in physics differently. Students' perceptions and how they frame the use of mathematics in physics play an important role in their physics problem solving. Hu and Rebello (2014) examined students' epistemological framing about using mathematics in physics in two types of problems: a conventional problem and a hypothetical debate problem. We found that when solving a conventional physics problem, students tended to frame problem solving in physics as rote equation chasing, i.e., plugging quantities into a memorized physics equation. In hypothetical debate problems, students were more likely to be involved in quantitative or qualitative sense making. We conclude that hypothetical debate problems might be used as an instructional tool for engaging students in sense making while using mathematics in physics. Thus, it might be potentially useful for developing more experts like problem solving expertise.

Education Research in Indonesia has begun to lead to the development of character education and is no longer fixated on the outcomes of cognitive learning. Derlina and Mihardi (2015) conducted a study aimed to produce character education based general physics learning model (CEBGP Learning Model) and with valid, effective and practical peripheral devices to improve character and learning outcomes of student. Character education is useful for forming learners of character and can solve the problems with actions that character. In addition, they produce a generation of competent and have good character in accordance with the expectations of education, especially in Indonesia. Developing of learning devices is done by 4D design, namely define, design, development and disseminate. The product prototype I validated by experts and practitioners, and then revised produced prototype II, then carried out a limited test in class. The Data was collected by learning outcomes test, questionnaire and observation sheet. The Data was analyzed statistically and descriptively. The Research results showed (1) validity of model quality is 3.96 (valid), (2) validity of lesson plan is 3.80 (valid), (3) validity of teaching materials is 3.59 (valid), (4) implementation of learning model is 77.50 (medium), (5) relevant aspects of student activity with learning activity is 80.23 (high), (6) student's response to the learning model is 83.45% (positive), (7) student's response to the learning devices is 87,50% (positive). The effectiveness of learning model shown from improvement cognitive learning outcomes and student character. The cognitive learning outcomes increased during the three meetings and character of students during the learning began to appear. The research results concluded that CEBGP Learning Model and supporting devices have fulfilled valid, practical and effective criteria.

Milner-Bolotin (2016) discussed how modern technology, such as electronic response systems, PeerWise system, data collection and analysis tools, computer simulations, and modeling software can be used in physics methods courses to promote teacher-candidates' professional competencies and their positive attitudes towards mathematics and science education. The discussion shows how modeling technology-enhanced deliberate pedagogical thinking in physics methods courses can improve teacher-candidates' subject-specific pedagogical knowledge and their positive attitudes towards science learning. They also discuss potential challenges that must be addressed in order to help teacher-candidates successfully implement these pedagogies during the practicum and in their early years of teaching.

Mkpanang (2016) investigated the influence of creative style and gender on students' achievement in physics. The sample was composed of one hundred (100) Senior Secondary II physics students was made up of 50 males and 50 females in Oruk Anam Local Government Area of Akwa Ibom State, Nigeria, and were administered by the Kirton Adaptor-Innovator Inventory (KAI), and the Physics Achievement Test (PAT) as instruments for the study. The three research hypotheses formulated to guide the study were



tested using multiple regression analysis. The results showed that gender and creative style as factors individually and jointly exert significant influence on students' achievement in physics. 23% of the variance in the achievement scores of the physics students was accounted for creative style, and 52% of the variance in the students physics achievement score was attributable to the joint influence of creative style and gender. Based on these findings, the paper recommended that explanation or prediction of achievement in physics test should take gender and creative style into consideration

In 2016, Hull and his colleges reported on data from the Colorado learning attitudes about science survey that suggests that Georgetown physics majors become increasingly expert in their attitudes towards physics learning and knowing after taking a course that combines two reformed curricula, Matter and Interactions (M&I) and Tutorials in Introductory Physics (TIPs). This occurs even though the two curricula do not send a consistent epistemological message to students. They analyze the interview video data of two students in a tutorial session to describe a possible mechanism that may have contributed to their growth. Finally, they compared the qualitative video data with quantitative data from the newly developed perceptions of physics classes’ survey and discussed aggregate responses to the survey to consider the ways in which other students developed more expert-like attitudes in this course. The study concluded that the attitudinal growth observed cannot be explained simply "as the result of" either M&I or of TIPs but rather find the most plausible explanation to be that the growth is an emergent phenomena produced by M&I and TIPs working together in concert with other factors.

3. RESEARCH PROBLEM STATEMENT AND QUESTIONS In the light of the aforementioned arguments, it became clear that there was a need to develop a distance e-learning program based on expert systems in physics and investigate its effect on scientific thinking skills among first secondary grade students in the Kingdom of Bahrain.

Thus, based on the problem statement, the main research question was formulated as follows: What is the effect of developing a distance e-learning program based on expert systems in physics (102) on Cognitive Achievement and scientific thinking skills among first secondary grade students in the Kingdom of Bahrain? Three sub-questions have been derived:

What is the cognitive achievement and scientific thinking skills that might be developed in physics (102) course among first secondary grade students in the Kingdom of Bahrain?

What are the design standards needed for a distance e-learning program based on expert systems for developing the cognitive achievement and scientific thinking skills in physics (102) among the first secondary grade students in the Kingdom of Bahrain?

What is the appropriate instructional design model for a distance e-learning course based on expert systems in physics (102) among the first secondary grade students in the Kingdom of Bahrain?

What is the effect of a distance e-learning program based on expert systems in physics (102) on developing cognitive achievement and scientific thinking skills of the first secondary grade students compared with those studying the same course in a distance e-learning course (not based on expert systems)?

4. RESEARCH METHOD The study applied the developmental research method that is appropriate for this research type. The developmental Research method (Elgazzar, 2014) integrates the following research methods:

Descriptive analytical research method: This method involves analysis of students’ characteristics, resources, content, and developing a list of design standards.

Systematic development method: This method involves implementation of instructional the instructional design model introduced by Khamies (2007).

Experimental research method: This method is used to carry out the research experiment to investigate the effect of developing a distance e-learning program based on expert systems on cognitive achievement, scientific thinking and problem solving skills.



5. RESEARCH PROCEDURES This research study followed the following steps:

Analysis of the Physics course content: The course content was divided into two chapters. The third chapter was entitled (accelerating movement) and it included five lessons, and the fourth chapter was entitled (Forces in one dimension) and it included seven lessons.

Deriving a list of standards for designing an e-learning course based on expert systems in order to develop cognitive achievement test and scientific thinking skills.

Developing a distance e-learning course in Physics based on expert systems using Khamis (2007) instructional design model.

Developing the research study instruments and verifying their validity and reliability. Such instruments included a cognitive achievement test in Physics, and a scientific thinking skills test in Physics.

Selecting sample of the study and dividing it into two experimental groups according to the research design. Administering the pretests of the study instruments (cognitive achievement test, scientific thinking skills

Test). Conducting the research experiment. Administering the post tests of study instruments (cognitive achievement test, scientific thinking skills Test). Recording, processing, interpreting data obtained, and selecting the appropriate statistical methods to test the

research hypotheses using (SPSS program). Presetting the study results, and proposing relevant recommendations, and suggestions based on those

results.

6. RESULTS

6.1. Results of the First Hypothesis Stated as Statistically significant differences (α ≤ 0.05) exist in the post application of the cognitive achievement mean scores between students of the first experimental group who studied content of the physics course using the e-learning program based on expert systems and students of the second experimental group who studied content of the physics course using the e-learning program that is not based on expert systems, and such differences were in favor of students of the first experimental group.

To test such hypothesis, means and standard deviations of the cognitive achievement post test scores of students of the first experimental group who studied content of the physics course using the e-learning program based on expert systems and students of the second experimental group who studied content of the physics course using the e-learning program that is not based on expert systems, were calculated. Table 2 shows that the cognitive achievement means score of students’ grades of the first experimental group are higher than that of students of the second experimental group. To verify such statistical differences, the independent sample t test was used, and Table 1 reveals the results of such analysis.

Table 2 Statistical significance t test results of the cognitive achievement mean score of students of the first and second experimental groups

Group Mean SD t df Sig. Level First experimental group 40.54 4.24 9.073 48 0.000 Second experimental group 20.72 10.27

The results reported in Table 1 revealed that significant statistically significant differences (α ≤ 0.05) existed between the post application of the cognitive achievement physics test mean scores of students of the first and second experimental groups, and such differences were in favour of the first experimental group as the t test value was 9.073 which is statistically significant at (α ≤ 0.05).



To test effectiveness of the e-learning program based on expert systems among students of the first experimental group, and verify its contribution to development of students’ physics cognitive achievement, the “effect size” was calculated using “2η” scale to determine the effect size of the independent variable which is the e-learning program on the dependent variable which is cognitive achievement of students in the physics course content.

After calculation of the t test, value of “2η” was calculated according to the following equation: t2

(Kiess: 1989, 446) 2η = t 2 + d f

t2 = refers to t square value resulting from comparing cognitive achievement mean score among students of the first experimental group in both pre and post test applications.

df: degree of freedom The following equation was used to calculate d value based on 2η value that represents the effect size

of the effectiveness of the e-learning program: 22 (kiess, 1989,445) d =

21 Appropriate statistical procedures were used to calculate 2η and d values, and Table 2 shows such

values.

Table 3 Value of 2η and its equivalent d value and the effect size of the e-learning program in developing cognitive achievement

Variable t value df Value 2η Effect Size Sig. Level Cognitive Achievement 9.073 48 0.627 2.59 Large Effect

Size * *Value of effect size = 2.59 or more indicates large effect size

Results of data analysis reported in Table 2 revealed that the significance level of 2η value of students’ cognitive achievement was high, as 2η value was 0.627 is considered to represent a high degree effect size which in turn indicated that the e-learning program contributed highly to development of students’ cognitive achievement.

6.2. Results of the Second Hypothesis Stated as Statistically significant differences (α ≤ 0.05) exist in the post application of the scientific thinking skills mean scores between students of the first experimental group who studied content of the physics course using the e-learning program based on expert systems and students of the second experimental group who studied content of the physics course using the e-learning program that is not based on expert systems, and such differences were in favour of students of the first experimental group.

To test such hypothesis, means and standard deviations of the scientific thinking skills post test scores of students of the first experimental group who studied content of the physics course using the e-learning program based on expert systems and students of the second experimental group who studied content of the physics course using the e-learning program that is not based on expert systems, were calculated. Table 3 shows that the scientific thinking skills mean score of students’ grades of the first experimental group is higher than that of students of the second experimental group. To verify such statistical differences, the independent sample t test was used, and Table 4 reveals results of such analysis.



Table 4 Statistical significance t test results of the scientific thinking skills mean score of students of the first and second experimental groups

Group Mean SD t df Sig. Level First experimental group 29.00 2.24 16.787 48 0.000 Second experimental group 13.88 3.98

Results reported in Table 3 revealed that significant statistically significant differences (α ≤ 0.05) existed between the post application of the scientific thinking skills physics test mean scores of students of the first and second experimental groups, and such differences were in favor of the first experimental group as the t test value was 16.787 which is statistically significant at (α ≤ 0.05).

To test effectiveness of the e-learning program based on expert systems among students of the first experimental group, and verify its contribution to development of students’ physics scientific thinking skills, the “effect size” was calculated using “2η” scale to determine the effect size of the independent variable which is the e-learning program on the dependent variable which is scientific thinking skills of students in the physics course content.

After calculation of the t test, value of “2η” was calculated according to the following equation: t2

(Kiess: 1989, 446) 2η = t 2 + d f

t2 = refers to t square value resulting from comparing scientific thinking skills mean score among students of the first experimental group in both pre and post test applications.

df: degree of freedom The following equation was used to calculate d value based on 2η value that represents the effect size

of the effectiveness of the e-learning program: 22 (kiess, 1989,445) d =

21 Appropriate statistical procedures were used to calculate 2η and d values, and Table 5 shows such

values.

Table 5 Value of 2η and its equivalent d value and the effect size of the e-learning program in developing scientific thinking skills

Variable t value df Value 2η Effect Size Sig. Level Scientific thinking skills 16.787 48 0.852 4.81 Large Effect Size*

*Value of effect size = 4.81 or more indicates large effect size Results of data analysis reported in Table 4 revealed that the significance level of 2η value of students’

scientific thinking skills was high, as 2η value was 0.852 is considered to represent a high degree effect size which in turn indicated that the e-learning program contributed highly to development of students’ scientific thinking skills.

6.3. Results of the First Question Stated as

6.3.1. Descriptive Statistics of the Distance E-learning Program Based on Expert Systems The researcher applied the descriptive statistics approaches and Table 5 shows the descriptive statistics to develop the distance e-learning program based on expert systems, where the arithmetic means and standard deviation are calculated for each of the following: Pre and post application of the cognitive achievement test. Pre and post application of the scientific thinking skills test.



Table 6 Arithmetic means and standard deviation of the descriptive statistics to develop the distance e-learning program based on expert systems

Variables

Developing the distance e-learning program based on expert systems

Experimental group 1 N = 25

Experimental group 1 N = 25

mean Standard deviation Mean Standard deviation

Prior application of the cognitive achievement test

13.65 3.79 11.32 6.25

Post application of the cognitive achievement test

40.54 4.24 20.72 10.27

Mean of the cognitive achievement 26.88 5.21 9.40 12.12 Prior application of the scientific thinking skills test

8.27 3.46 8.48 4.50

post application of the scientific thinking skills test

29.00 2.24 13.88 3.98

Mean of scientific thinking skills acquisition test

20.73 4.03 5.40 6.35

Table 6 shows that: All students got means of scores less than (40%) in the prior acquisition of the cognitive achievement test.

Students who studied the physics course using the distance e-learning program based on expert systems obtained means of scores (13.65) while students who studied physics course using distance e-learning program obtained means of scores (11.32) as the previous means are less than (40%) out of the final mark of the acquisition of the cognitive achievement test (53). This is because students had not studied the cognitive content of the subject earlier.

Students who studied the physics course using the distance e-learning program based on expert systems obtained means of scores (40.54) in the post application, which is more than (75%) out of the final mark of the acquisition of the cognitive achievement test (53), while students who studied physics course using distance e-learning program obtained means of scores (20.72) which is more than (38%) of the final mark of the acquisition of the cognitive achievement test.

All students got means of scores less than (25%) in the prior application of the scientific thinking skills test. Students who studied the physics course using the distance e-learning program based on expert systems obtained means of scores (8.27) while students who studied physics course using distance e-learning program obtained means of scores (8.48) as the previous means are less than (25%) out of the final mark of the Prior application of the scientific thinking skills test (33). This is because students had not studied the cognitive content of the subject earlier.

Students who studied the physics course using the distance e-learning program based on expert systems obtained means of scores (29.00) in the post application, which is more than (85%) out of the final mark of the scientific thinking skills test (33), while students who studied physics course using distance e-learning program obtained means of scores (13.88) which is more than (40%) of the final mark of the scientific thinking skills test.

6.3.2. Answer of Sub question 1 To answer sub question 1 which is: “What are the cognitive aspects and scientific thinking skills and problem solving skill that can be developed in physics subject in the high secondary school?”, the researchers derived the cognitive and scientific thinking skills covering (Accelerating movement, forces) of Grade 10 and included 12 lessons and 62 objectives which were divided into 17 Remembering, 8 understanding, 20 application, 14 Analysis, 3 Evaluation.



Table 7 Characteristics of the analysis of the contents of chapters 3 and 4 (Accelerating movement, forces) of Grade 10 – physics

6.3.3. Answer of Sub question 2 To answer this sub question 2 that states “What are the design standards for a distance e-learning course based on expert systems to develop the cognitive and scientific thinking skills?”, the researcher developed a list of the design standards for a distance e-learning course based on expert systems environment consisting of 15 standards and including 113 indicators. The researchers developed such developed them through reviewing the previous Arabic and foreign literature that studied the design of materials and environment of a distance e-learning course based on expert systems. A group of referees specialized in the education technology reviewed and amended them as indicated in Table 8.

Cha

pter

L

esso

n

Obj

ectiv

es

Levels of Thinking according to Bloom Classification

Rem

embe

ring

Und

erst

andi

ng

App

licat

ion

Ana

lysi

s

Eva

luat

ion

Cre

atin

g

Cha

pter

3

(acc

eler

atio

n m

ovem

ent)

Lesson 1 (Acceleration) 5 2 1 0 1 1 0 Lesson 2 (Speed time curve) 3 1 0 1 1 0 0 Lesson 3 (Constant acceleration motion)

3 1 0 1 1 0 0

Lesson 4 (Equations of Motion for Constant Acceleration)

5 0 0 2 3 0 0

Lesson 5 (Horizontal and vertical motion equations)

5 2 0 2 1 0 0

Cha

pter

4

(For

ces i

n on

e di

men

sion

)

Lesson 1 (Strength and Movement)

5 1 0 2 1 1 0

Lesson 2 (Gathering Powers) 6 1 2 2 1 0 0 Lesson 3 (The Weight) 3 1 0 1 1 0 0 Lesson 4 (Newton's Second Law)

8 3 2 2 1 0 0

Lesson 5 (Drag Force and Terminal velocity)

4 2 1 1 0 0 0

Lesson 6 (Applications of Newton's Second Law)

11 3 1 4 2 1 0

Lesson 7 (Newton`s Second Law of Motion and Equations of Motion with Regular Acceleration)

4 0 1 2 1 0 0

Total 62 17 8 20 14 3 0



Table 8 A list of the design standards for a distance e-learning course based on expert systems

Standard 1: A distance e-learning course based on expert systems is designed in the light of learning objectives developed in line with the ILOs. Standard 2: A distance e-learning course based on expert systems is designed to achieve the specified learning objectives. Standards 3: All texts in a distance e-learning course based on expert systems will be clear and readable and free of grammatical and scientific mistakes. Standard 4: Illustrations and pictures in a distance e-learning course based on expert systems will be clear and simple. Standard 5: Video clips in a distance e-learning course based on expert systems will be functional and simple. Standard 6: A distance e-learning course based on expert systems will include a good standard of interactions that enable a learner to take part in the learning activities effectively.

Standard 7: Learning tasks and activities will be designed as to include feedback that is in line with the scientific thinking and problem solving skills development. Standard 8: A distance e-learning course based on expert systems is designed to achieve aspects that are easy to use by learners. Standard 9: A distance e-learning course based on expert systems is to achieve as much benefit as possible to enable learners to solve problems they face in physics. Standard 10: A distance e-learning course based on expert systems is to achieve a level of quality of functions for a learner. Standard 11: The façade of a distance e-learning course based on expert systems is designed to achieve the competency of a learner in controlling the program and browsing in solving the physical problems. Standard 12: A distance e-learning course based on expert systems is designed with a programming structure to achieve a balance and stability to limit mistakes and time of processing of tasks. Standard 13: A distance e-learning course based on expert systems is designed to economically help a learner in his learning process. Standard 14: A distance e-learning course based on expert systems is designed to achieve the expected findings and learning outcomes. Standard 15: A distance e-learning course based on expert systems is designed so as the program provided advice and guidance for a learner based on his/her response and previous knowledge.

6.3.4. Answer to the Sub question 3 To answer the sub question 3 stating: “What is the learning design of a distance e-learning course based on expert systems types in physics in the secondary school according to the previous standards?”, the researcher specified the list of the design standards of a distance e-learning course based on expert systems. The model of Mohamed Attiya Khamis (2007) for learning design to design and develop an e-learning program in a distance e-course based on expert systems was applied. This was shown in Chapter 3.

6.3.5. Answer of Sub question 4 To answer the sub question 4 stating: “What is the impact of developing a distance e-learning course based on expert systems of a physics course in developing:

The cognitive achievement by comparing learners who study this e-course (without expert systems). The researcher tested the validity of the first and fifth hypothesis to answer this question using SPSS and the statistical tools indicated in chapter 3 as it will be illustrated in the validity of hypothesis section.



The scientific thinking skills by comparing learners who study this e-course (without expert systems). The researcher tested the validity of the second and seventh hypothesis to answer this question using SPSS and the statistical tools indicated in chapter 3 as it will be illustrated in the validity of hypothesis section

7. RECOMMENDATIONS: In the light of results of the study, the researcher offered the following recommendations:

Use of e-course based on expert systems in other instructional subjects besides physics. The use of e-learning programs based on expert systems lead to development of other types of thinking such

as critical thinking. Utilization of Mohammed Attya Khamis (2007) instructional design model as it proved its effectiveness on

this field. Use of the list of design standards to develop e-learning programs based on expert systems and intelligent

systems. Encouragement of designers of instructional programs to utilize artificial intelligence methods such as expert

systems in developing instructional materials.

8. RESEARCH SUGGESTIONS After presenting the results, the researcher suggested the following:

Conducting research studies similar to the current study to investigate their effect on other types of thinking such as critical thinking.

Conducting research studies in other fields of studies such as Mathematics, Chemistry and Biology. Conducting research studies similar to the current study in personalize learning environments.

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