About this paper

Appears in:
Pages: 2922-2929
Publication year: 2016
ISBN: 978-84-608-5617-7
ISSN: 2340-1079
doi: 10.21125/inted.2016.1661

Conference name: 10th International Technology, Education and Development Conference
Dates: 7-9 March, 2016
Location: Valencia, Spain

CLUSTER ANALYSIS OF DATA LOGS GENERATED BY INTELLIGENT TUTOR TO DETERMINE STUDENTS' LEARNING PROFILES

A. Dani

Higher Colleges of Technology (UNITED ARAB EMIRATES)
Quality of learning experience that students gain in the foundation year of their higher studies is important as it lays the foundation for their learning in the later years. The ability to learn independently must be developed in the foundation year which will be applicable in the later years in the study of their chosen major. In the foundation year, students experience transition from school to university. They have fewer face-to-face teaching sessions with their instructor than they have in school. In some cases, students are uncertain about what is expected of them and in such cases formative feedback from the instructor can guide them. Formative assessments are designed for providing feedback on actual learning and they can fill the gaps between the actual learning and expected learning outcome. The results of formative assessment must be available immediately to make adjustment in teaching and learning [4,6,8,9]. It may not be practical to incorporate frequent formative assessments due to some reasons, such as big class sizes and limited number of face to face teaching sessions. To overcome these challenges, computer based learning environments, such as intelligent tutors are widely used in high schools and universities in many countries including the United Arab Emirates (UAE).

Intelligent tutors can integrate more than one medium, provide authentic learning activities and can be used to provide support to many students at the same time in a classroom setting. Intelligent tutors provide superior performance than any other computer assisted instruction program because they are developed by combining theories of cognitive science and techniques of artificial intelligence. Intelligent tutoring system, such as ALEKS (which is an abbreviation of Assessment for LEarning using Knowledge Spaces) are available online and accessible on all devices, such as, laptops, iPads and smart phones. These tutoring software systems provide personalized tutoring and a scope for learning at an individual pace [5,6,7].

Expert human tutors possess the knowledge of the subject, knowledge about students’ strength and weaknesses and ability to provide appropriate feedback and scaffolding based on the knowledge of the subject and the student. After the emergence of sophisticated techniques of artificial intelligence, it is possible to embed three characteristics of human tutors into the tutoring software, which are knowledge of the subject, knowledge of the student and knowledge of teaching [1,2,4]. Intelligent tutor like ALKES, presents problems based on student’s current knowledge. A student can solve a number of problems until the software detects that student has mastered the topic. Evaluation and feedback is given instantly by the software. More importantly, software can generate questions randomly from a large question bank. Random based assessments not only control copying and cheating but also provide ample practice questions required for mastering a topic. This type of web-based assessment software can foster the student-centered learning and skills of independent learning. Another advantage of such software is that it maintains detailed logs of students’ learning activities, such as how many topics were practiced, how may topics were mastered successfully and how much time was spent by each student.
@InProceedings{DANI2016CLU,
author = {Dani, A.},
title = {CLUSTER ANALYSIS OF DATA LOGS GENERATED BY INTELLIGENT TUTOR TO DETERMINE STUDENTS' LEARNING PROFILES},
series = {10th International Technology, Education and Development Conference},
booktitle = {INTED2016 Proceedings},
isbn = {978-84-608-5617-7},
issn = {2340-1079},
doi = {10.21125/inted.2016.1661},
url = {http://dx.doi.org/10.21125/inted.2016.1661},
publisher = {IATED},
location = {Valencia, Spain},
month = {7-9 March, 2016},
year = {2016},
pages = {2922-2929}}
TY - CONF
AU - A. Dani
TI - CLUSTER ANALYSIS OF DATA LOGS GENERATED BY INTELLIGENT TUTOR TO DETERMINE STUDENTS' LEARNING PROFILES
SN - 978-84-608-5617-7/2340-1079
DO - 10.21125/inted.2016.1661
PY - 2016
Y1 - 7-9 March, 2016
CI - Valencia, Spain
JO - 10th International Technology, Education and Development Conference
JA - INTED2016 Proceedings
SP - 2922
EP - 2929
ER -
A. Dani (2016) CLUSTER ANALYSIS OF DATA LOGS GENERATED BY INTELLIGENT TUTOR TO DETERMINE STUDENTS' LEARNING PROFILES, INTED2016 Proceedings, pp. 2922-2929.
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