2015 IEEE 15th International Conference on Advanced Learning Technologies
A Feedback Effectiveness Oriented Math Word Problem E-Tutor for E-Learning Environment Kyle Morton
Yanzhen Qu
School of Computer Science Colorado Technical University Colorado Springs, USA
[email protected]
School of Computer Science Colorado Technical University Colorado Springs, USA
[email protected]
Abstract- E-Learning is gaining more traction as it is accepted and used by more students, as it provides time convenience, cost effectiveness, and location flexibility. E-Learning’s key weakness is lacking of a cost effective way to support instructors to provide synchronous feedback to students. In addition, the help provided is usually not in real time and is missing an instructor’s influence to a student’s affect status in the feedback, which creates a practicality gap between e-Learning and feedback effectiveness. This paper proposes an e-Tutor framework for math word problem, and discusses various aspects of feedback effectiveness which is the central design concept of the e-Tutor.
them; and (c) student’s affect status during the learning. Without accounting for all three components of student’s learning ability, feedbacks provided by an instructor in an eLearning environment often cannot be very effective [2]. C. What is Feedback Effectiveness The effectiveness of feedback in any type of learning environment can be defined by the responsiveness of the feedback: specifically, the time lapse between when the student proposes the question to when the student receives feedback, and the accuracy of the feedback [4].
Keyword – e-Learning; e-Tutor; feedback effectiveness.
II. PROBLEM STATEMENT AND HYPOTHESIS A. Problem Statement In e-Learning environment, students do not have the same in-person experience feedback effectiveness as those in a traditional face to face learning setting. Typically, most feedback by an instructor is not very effective because not only delayed communication, but also lacking full knowledge about the student’s learning ability, which is reflected by student's cognitive ability, learning style and real time affect status.
I. INTRODUCTION In a traditional face to face classroom based learning environment, all parties involved in education are gathered in a common physical location, and the content is delivered by the in-person face to face communication. In the recent decade, due to advancement in computer technology, a new type of learning environment has gained popularity, where all parties involved in education are gathered on a computer network, and the content is delivered through various types of computer network based communication such as email, blogging based discussion boards or live chat, streaming video, etc. This learning environment is called e-Learning or distant learning [1].
B. Hypothesis If we can build an e-Tutor system that is not only available to students in an e-Learning environment at any time, but also capable of providing effective feedback by integrating the data that reflects all aspects of a student's learning ability, then the effectiveness of the feedback provided by an e-Tutor in an eLearning environment will be similar to the feedback received in a face to face learning environment.
A. Challenges of E-Learning Environments Though e-Learning environment has many advantages, it also has some severe weaknesses too. Two important disadvantages are (a) absence of cost effective capabilities to support instructors to provide synchronous feedback to students when they need help, and (b) the majority of feedback provided is missing the influence of instructors’ reaction to students’ affect status, which is important when understanding the student’s cause for a mistake.
III. METHODOLOGY To prove the hypothesis, in this paper we have proposed a framework for an e-Tutor system that is capable of providing effective feedback on math word problems to students, assisting them to correct the errors that occur during the process of solving a math word problem. The basis of this eTutor is to have the ability to use a student’s learning ability and a math word problem’s difficulty level to accurately determine the most effective feedback approach for a mistake made by the student. In providing effective feedback to students, we have used unsupervised machine learning algorithm to train the e-Tutor system by using the data
B. Student’s Learning Ability Students react to the challenges in e-Learning differently depending on each individual’s learning ability, which consists of three aspects: (a) student’s comprehensive cognitive capability or performance in class; (b) student’s learning style such as visual, auditory, tactile or a hybrid of 978-1-4673-7334-0/15 $31.00 © 2015 IEEE DOI 10.1109/ICALT.2015.40
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of the e-Tutor system. One off experiments is to assess what would be the most appropriatte feedback provided by the eTutor under various situatioons. For example, the most effective feedback for a studeent with an excellent cognitive ability and in excited emotion is i the feedback that matches the learning style for the first mistake, m and the feedback that addresses the cognitive perfoormance issue for the second mistake. A student with excelleent cognitive ability and in tired emotion should be given feeddback that matches the learning style for the first mistake, and the feedback that addresses the affect status for the second misstake. Furthermore, for students who are not excellent in cognittive ability, their emotion status such as excited or tired shouldd receive more weight than their learning style considerations when w providing feedback to such students.
reflecting three aspects of a student’s learning ability introduced in [4] and the math word problem mistake recognition method presented in [3]. A. E-Tutor Framework The framework provides a prototypiccal structure that describes the processing of a student’s learnning ability, which includes how a student’s learning ability caan be detected and weighted to determine the most effective/acccurate feedback by using ontology, machine learning, and fuzzy logic. An w problem for ontology is created to represent the math word providing feedback unique mistake sppecific feedback. Formally an ontology is defined as a comm mon vocabulary for sharing information specific to a domain, which w we use it to include machine-interpretable definitions of o concepts in the domain and relations among a math wordd problem; where ontology also build personalized e-Learning experiences [4, 5, 6]. Moreover, five components of the e-Tuttor system contain the processes that execute the algorithm ms for providing effective feedback when a mistake has been made by the o the Human – student. The five components consist of Computer Interaction component (HCI), input component, observation component, delivery feedbackk component, and assessment component. Figure 1, displays the life cycle of tutoring a student for each component in the e-Tutor system.
CLUSION V. CONC
The e-Tutor framework proposed in this paper has provided the evidence that effeective feedback can be provided to students by 1) learning and a understanding a student’s learning ability, 2) properly coomputing an accurate difficulty level for a math word problem m, and 3) determining effective feedback by learning a studennt’s mistakes. The results reveal that over 85% of the feedbback provided by the e-Tutor accurately addressed all studeent mistakes. This result shows that effective feedback is a vital aspect of student learning, as i the key factor to improving a understanding one’s mistakes is student’s learning ability. Mooreover, the affect status of a student was a factor in the learrning ability, as this e-Tutor has used a student’s affect statuus to accurately determine the emotional needs of the student.. REFERE ENCES [1]
C. Chua and J. Montalbo, "Asseessing Students’ Satisfaction on the Use of Virtual Learning Environmentt (VLE): An Input to a Campus-wide Elearning Design and Implemenntation," Information and Knowledge Management, vol. 3, 2014, pp 1008 - 116
[2]
P. Rodriguez, et al., "Extractingg Emotions from Texts in E-Learning Environments," in Complex, Inteelligent and Software Intensive Systems (CISIS), 2012 Sixth Internationall Conference on, 2012, pp. 887-892. Y. Qu and K. Morton, " A Noveel Approach of Providing Feedbacks at Where Mistake Occurs During Solving Math Word Problems, Proceedings of 2013 Internaational Conference on Information Technology and Computer Appplication Engineering (ITCAE), Hong Kong, China, August 27-28, 20133, pp 203 - 209 Y. Qu and K. Morton, "On Design and Development of an Effective Math Word Problem Oriented E-Tutor", In Proceedings of the 2nd International Conference on Eduucation Technologies and Computers", Bangkok, Thailand, May 20 - 22,, 2015
[3]
Figure 1. E-Tutor System Process Flow [4]
The algorithmic procedure of the e-Tuttor application for providing effective feedback details three maain aspects, which are: unsupervised machine learning, ontologyy, and fuzzy logic. The machine learning aspect of this algoritthm purpose is to recognize what mistake was made. After a machine learning update, fuzzy logic [7] is used to determine the most effective feedback for the given mistake conditioon. The ontology representing the math word problem is ussed to generate a feedback specific to the mistake made by thee student.
[5]
D. L. M. Natalya and F. Noy, "O Ontology Development 101: A Guide to Creating Your First Ontology", Available: A http://www-ksl.stanford.edu/peopple/dlm/papers/ontology-tutorial-noymcguinness.pdf [6] F. v. H. Deborah and L. McGuinness, "OWL Web Ontology Language Overview", Available: http://ww ww.w3.org/TR/2004/REC-owl-features20040210/ [7] T.J. Ross, "Fuzzy Logic with Enggineering Applications", 3rd Edition,
IV. RESULTS To validate the framework proposed in thhis paper, we have conducted several experiments that targetedd the functionality
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