20-10-2016, 09:18 AM
1459964041-111.docx (Size: 144.83 KB / Downloads: 4)
ABSTRACT
The Artificial Intelligence technique is used in evaluating a student’s understanding of a particular topic of study using concept maps .For the same purpose, a tool called Artificial Intelligence-based student learning evaluation tool (AISLE) is used. The probability distribution of the concepts identified in the concept map developed by the student is calculated. The evaluation of a student’s understanding of the topic is assessed by analyzing the curve of the graph generated by this tool.
Index Terms—Concept Maps, Evaluation, Probability Distributions, XML Parsers
1 INTRODUCTION
CONCEPT maps, which are visual representations of a particular topic and its subcomponents, have been used in multiple settings to teach information. The power of the concept map lies in the fact that it requires the elucidation of the relationships between the subcomponents of a particular topic. The effectiveness of using concept maps for knowledge retention over other forms of summarizing information has been demonstrated in multiple studies and in naturalistic settings . In addition, concept maps can be used as a form of evaluation of student learning . When a particular topic is taught, concept maps can be utilized to determine what the student knows about a subject, rather than using more traditional forms of assessment such as multiple-choice exam. We are in the process of developing a tool to evaluate student learning using concept maps . Here, a student would be given a topic to learn and build a concept map based on their understanding of the topic. This tool, named Artificial Intelligence Based Student Learning Evaluation Tool (AISLE), would then evaluate the concept map and assess if the student has captured enough concepts from the given topic. This will help the instructor in evaluating a student’s understanding of the topic.
The objective of this project is as follows:
• To develop a tool that understands student psychology in terms of the learning process undertaken by student using concept maps.
.
This project can have the following impact on the academic community:
• It will provide a better understanding of the student learning process, which will have practical curriculum and classroom applications for educational psychologists [10].
• The project will provide the school districts in northeast Texas with a new educational tool to use in their classrooms.
The research question targeted in this project is as follows: “Can we use a concept map-
based approach in validating student performance?” While many concept map-based approaches have been proposed for assessing a student’s knowledge of a particular topic, AISLE provides the following core contribution: “Development of a comparitive analysis using probability distribution to compare concept maps developed by students.” In this paper we first discuss some related work. We then present a detailed discussion on methodology involved in using AISLE and details pertaining to the processing involved with algorithms, examples and details of the analysis of the input. To conclude, we provide results of experimentation, comparison with related tools and sections describing the usefulness of this tool.
1.1 Related Work
1.1.1 Intelligent Knowledge Assessment System
The Knowledge Assessment System provides a structured approach to asssessing a student’s knowledge on a particular topic. The software application associated with this system presents questions to the user and generates an analysis using the answers provided to these questions. This system uses a well defined structured approach in gathering the required information and performing the required analysis. Moreover, this system provides feedback to the student as well as the teacher. Some of the key contributions of this system are:
a) Providing necessary feedback to students in restructuring their acquired knowledge,
b) Providing feedback from the teacher to the student by using the system.
1.1.2 Personalized Assessment System Supporting Adaptation and Learning
The Personalized Assessment System Supporting Adaptation and Learning (PASS) provide an assessment of a student’s present knowledge and helps identify the knowledge areas that the student may not have covered. This helps the student analyze the progress made in the learning process and identify the areas where more learning may be required. Some of the key contributions of this system are:
a) identification of prior knowledge of the student;
b) diagnosis of concepts unknown to the student;
c) identifying the growth in a student’s overall understanding of the topic.
1.1.3 Knowledge Assessment System
The Knowledge Assessment System presented makes use of the concept maps developed by domain experts in analyzing a student’s understanding of the concepts. It makes a comparison between these concept maps and the concept map developed by the student. This approach is based on the assumption that the concepts identified by the experts would represent the complete knowledge domain while the concept map developed by the student would be somewhat incomplete.
1.2 Concept maps
Concept maps are graphical tools for organizing and representing knowledge. They include concepts, usually enclosed in circles or boxes of some type, and relationships between concepts indicated by a connecting line linking two concepts. The characteristics of concept maps are as follows:
• The concepts are represented in a hierarchical fashion with the most inclusive, most general concepts at the top of the map and the more specific, less general concepts arranged hierarchically below.
• It includes” cross-links “. These are relationships or links between concepts in different segments or domains of the concept map.
1.3 XML Document
Extensible Markup Language (XML) is a markup language that defines a set of rules for encoding documents in a format which is both human-readable and machine-readable. The characters making up an XML document are divided into markup and content , which may be distinguished by the application of simple syntactic rules. Generally, strings that constitute markup either begin with the character < and end with a >, or they begin with the character & and end with a ;.Strings of characters that are not markup are content.
Tags
A markup construct that begins with < and ends with >. Tags come in three flavors:
Start tags; for example: <section>
End-tags; for example: </section>
Empty-element tags; for example: <line-break/>
2 METHODOLOGY FOR USING AISLE
As indicated previously, the method used by the tool to evaluate student understanding of specific topics as discussed in the class is different than the regular methods used, with differing qualities such as quizzes, oral presentations, and projects. While most instructors attempt to measure a student’s understanding of topics discussed in the class by personally evaluating the student’s work, this tool automates the task of evaluation. The method used by the tool can be used by having a deeper measurement of a student’s understanding of the domain in discussion by inspecting the areas of the domain concepts in which the student may be interested . Figure 1 provides a brief description of the methodology involved in using our tool. As reflected in figure 1, this methodology involves the following steps:
• Students develop concept maps after studying the prescribed material.
• These concept maps are converted into XML-based documents .
• The XML analyzer module of our tool extracts the concepts embedded in the XML document.
• The analysis module makes an assessment of the importance of the concepts captured by the students and provides a summary of the results of the user interface module.
IMPLEMENTATION
The steps involved in the evaluation procedure are as follows:
3.1 Concept map development by the student
The student learns a topic thoroughly and thereby develops a map by dividing the main concept into several sub concepts, each with a relationship between them. The concept map developed by the student is considered to be incomplete. The map is a hierarchical tree like structure having a root and one or more children for each node at various levels.
3.2 Conversion of concept maps into XML document and its parsing
The concept map developed by the student is converted into an XML-based document. This XML file contains all the required information on concepts and their associated relationships. The linking phrase between any two concepts represents the relation and the technique, which comprehends these relations among different concepts, and is called “Concept Mapping”. In concept mapping, we identify the concepts and hierarchically organize these concepts and differentiate the concepts into different levels where the lower most level represents the important information in understanding the topic.
The XML analyzer then parses the XML document. A suitable parser is used to extract concepts and relations from the XML file. Each and every concept is assigned with a unique “concept id” which helps the developers in programmatically extracting the concept name by using these “concept ids”. By using these XML files, software developers write programs to extract the “concept id” and its corresponding “label” concepts present in concept map.
The XML file contains a tag with “connection-id”,”from-id”, and “to-id”. Here, the “connection-id” is a unique id that represents the linked form of concepts. For example, we have a concept “War of American Independence” related with “Boston campaign” using the relation “includes.” Here, two unique connection-ids are created: one describes the connection from “War of American Independence” to “includes,” and the other describes the connection from the relation “includes” to the concept “Boston campaign”. In order to identify the connection between the concepts, the XML file creation process creates a connection-id that helps in identifying the connection. The XML file creates this connection in the form of “concept->relation” and “relation ->concept”.
3.3 Analysis of Concept Maps
AISLE is a tool that helps the instructors analyze a student’s understanding of a particular topic. The instructor uses the concept map and runs it on AISLE to receive the statistics based on probability density function. The ids “from-id” and “to-id” are used to represent all the connections between the concepts to the relation and relation to the concepts that forms a link from one concept to another concept from the concept maps. The concept map is divided into mainly three levels and a concept can be in any one of the following levels:
o A concept is said to be in Level 0 if the concept-id is only present in “from-id”.
o A concept is said to be in Level 1 if the concept-id is present in “from-id” and “to-id”.
o A concept is said to be in Level 2 if the concept id is only present in “to-id”.
There is always one concept that is always present in the Level 0 and it is always the root node of the concept. The concepts after the root node are considered to be in Level 1 and concepts interlinked to these Level 1 concepts are found in Level 2.The concepts beyond Level 2 will be recursively and iteratively identified as either a Level 1 or Level 2 concept.
3.4 Scoring individual concepts
The scoring analysis is carried out in two steps:
1) First, a random score is given for each concept represented in the concept map.
2) Second, a random score value depends on the level of the concepts in the hierarchy.
The scoring system is as follows:
1) 1)All the concepts at Level 1 are given equal increments of a random score to each of the concepts.
2) A concept at Level 0, which happens to be the root node, is given a random score after the scores are assigned to Level 1 concepts.
3) All the concepts in the Level 2 are given an equal increment of random scores after assigning the score to Level 0 concepts.
3.5 Evaluation of Concept Maps
The standard probability distribution of the curve is used as a reference curve to evaluate the concept maps drawn by the students. The concept map drawn by the student must be verified and validated by the instructor. First, calculate the z-scores for all the concepts present in the hierarchy and standard probability distributions using the formulae:
Zconcept=(Scoreconcept-Meanofconceptscores)/StandardDeviationscore
where StandardDeviationscore≠0
The standard form of probability distribution function in AISLE is given by the equation
P (concept) =1/StandardDeviationscore√(2π) *
e-(scoreconcept-Meanofconceptscores)2 / 2*(StandardDeviationscore)2
where StandardDeviationscore≠0 & 0<scoreconcept<∞.
CONCLUSION
The proposed methodology enables an instructor to evaluate a student’s understanding of a particular topic taught in the class. It also enable him to compare the performances between the students. Nevertheless, it also enables an instructor to analyze the progress in the learning procedure of a particular student. All these could be done easily and with better efficiency using the AISLE tool.