LEARNING MEASUREMENT MODEL FOR ELECTRONIC LEARNING SYSTEM USING VELOCITY, QUANTITY, AND RELEVANT ANSWER PARAMETERS
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...
Saved in:
| Main Author: | |
|---|---|
| Format: | Dissertations |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/53478 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | 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. |
|---|