LEARNING MEASUREMENT MODEL FOR ELECTRONIC LEARNING SYSTEM USING VELOCITY, QUANTITY, AND RELEVANT ANSWER PARAMETERS

• Main Author: Juliane, Christina
• Format: Dissertations
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/53478
• Institution: Institut Teknologi Bandung

Teaching and learning activities have evolved rapidly through the development of information and communication technology. The need for a concept and mechanism of ICT-based teaching and learning seem to be inevitable, because its presence provides flexibility in the choice of time and place, and reach a broader populations of study participants that are not limited by space, cost, and resources. The concept collaborating ICT into teaching and learning process, better known as e-learning activity comes to be a breakthrough in the world of education. E-learning technology transform in other forms such as Massive Open Online Course to reach enormous amount of learners. This learning platform enables anyone to participate anywhere with open access and interactive user services with the main vision to reduce the cost of education and be able to reach large participants. MOOC provides various kinds of benefit that are worthwhile for its users. But there are also numbers of issues, such as the problem of dropout rates that are quite high among students of MOOC users caused by their low learning retention scores. It occurs as the result of low quality of acces, content, learning, and pedagogy of the MOOC system. The main reason causing the problem resulting from the high dependence of the system on the existence of large numbers of Human Resources and qualified evaluation method that can evaluate the impact of teaching learning process through MOOC. The use of technology in the learning and teaching is to accelerate the process in order to gain the value of effectiveness, efficiency, and innovation. But somehow the facts that occurred, technology support does not assist the essential meaning of teaching learning process, specifically the behavioral changes and addition of knowledge as the evidence of success process. Because in essence a teaching and learning both traditional and ICT-assisted is an activity of sending and receiving new knowledge which then impacts on changes in behavior, awareness, and perception. However, to identify additional knowledge and behavior change is not easy because it takes time and strategy and is become another issue that needs to be resolved. One way to identify the issues is by measuring the learning outcomes obtained by students. The research conducted, aims to build a model of measuring the achievement of learning outcomes in an e-learning system environment, that can identify the changes in behavior and the addition of knowledge automatically in order to increase the value of the effectiveness and efficiency of the measurement process. This was carried out as an effort to increase public confidence that the quality of learning in a MOOC environment was equivalent to face-to-face learning, cheaper because it could reach larger students, flexible, effective, and efficient in the process. This research conducts the process of identifying the existing conditions of the e-learning environment to view the conditions, challenges, and opportunities that exist. As well as the process of identifying the parameters and rules of measurement used in the research model. The main contribution in this research is the learning outcome measurement model for e-learning system with the supporting contribution are the identification of the parameters and rules that can be used to assess the behavior change process, and expansion of knowledge to measure achievement of learning outcomes in the elearning system. In the stage of identification of parameters in measurement models, six parameters produced that can be used in the model, which are the speed of downloading, uploading, and answering questions, the quantity of words in the given answer sentence and the relevance of the answers given related to the correctness of the answers and the suitability of the answers to the thinking level of Bloom's Taxonomy. Identify the behavior change process can be performed by the parameters of the speed of downloading, uploading, and answering questions, and the quantity of words in the given answers. Meanwhile, to identify the addition of knowledge can be performed by applying the parameters of the relevance of the answers that will be seen from the correctness and the suitability to the thinking level. Another contribution is the production of eleven rules that can be used to measure learning outcomes in e-learning systems. The rules are classified into eight rules to identify the person has learned and three other rulesto identify that has not as a result of teaching and learning process in e-learning system. The parameters and rules produced are tested and evaluated by implementing the model in the form of an exam module in e-learning. The model testing and evaluate by comparing the results of the assessment carried out by the sistem and experts that consisting of competent teachers and lecturers. The accuracy of the test results is then compared with the accuracy of a baseline version. The result showed that accuracy of the model better than a baseline version, which is an assessment based on the attribute of r (relevance), consist of attribute r1 (correctness of the answer) and r2 (conformity of the answer with thinking level of Bloom's taxonomy). These attributes was chosen as the baseline with the consideration that the assessment of teaching and learning process is generally done intuitively by assessing the correctness of the answers and the students thinking level. This indicates that the research model can be used to measure the achievement of learning outcomes in elearning systems in the form of behavior changes and knowledge addition. The behavior changes, identified from speed of uploading (v1), downloading (v2), and answering questions parameter (v) and the quantity of words in the given answer sentence (q). While for the addition of knowledge, identified from the parameters of i the relevance of the answers related to the correctness of the answer (r1) and the suitability of the answer (r2) with the standards of Bloom's Taxonomy.