Utilizing Concept Mapping in Intelligent Tutoring Systems - Springer

Utilizing Concept Mapping in Intelligent Tutoring Systems Jaclyn K. Maass and Philip I. Pavlik Jr. Institute for Intelligent Systems and Department of Psychology, University of Memphis, Memphis, TN, USA {jkmaass,ppavlik}@memphis.edu

Abstract. Concept mapping is a tool used in many classrooms and highly researched in the field of education. However, there are fewer concept mapping studies in the field of artificial intelligence in education, specifically within intelligent tutoring systems. Two studies highlight the important roles that concept maps and other non-linear organizers play in learning. Concept maps provide students with a macrostructure view of the information as well as allow students to easily see relationships between concepts. Students generating material for a concept map has shown high learning gains; however, students creating maps from scratch or students being provided a completed map has not seen such positive effects. The proposed study looks at the importance of the links, or relationships between concepts, within concept maps. We plan to provide students with partially filled in concept maps as note-taking devices to investigate how much and what kind of assistance or scaffolding is needed. Keywords: concept map, intelligent tutoring system, scaffold, note-taking.

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An Introduction to Concept Mapping

Concept mapping as a learning tool was first introduced by Novak and Gowin [1]. They showed that students could use concept maps to learn how to learn effectively. Since then, there have been many other studies showing the advantages of using concept mapping in the classroom, particularly within the science domain. The basic components of a concept map are nodes, which display the main ideas or concepts, and links which connect the nodes and depict the relationships between concepts. Each node-link-node connection is called a proposition [1]. There is a clear difference between concept maps, which are spatial in nature, and more traditional, linear outlines, which do not lend themselves toward comparison or explicit learning of relationships among concepts. Concept maps allow for faster and easier access to: the location of information within the larger arrangement [2], relationships between concepts [3], and the macrostructure of the information [4].

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Combining Concept Mapping and Artificial Intelligence

Educational artificial intelligence materials have lacked an emphasis on the use of concept maps. There are two instances we will discuss here in terms of the different K. Yacef et al. (Eds.): AIED 2013, LNAI 7926, pp. 880–883, 2013. © Springer-Verlag Berlin Heidelberg 2013

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ways they utilized concept maps within intelligent tutoring system (ITS) structures. We will then suggest a different use for concept maps in ITS’s to advance the field and aid in student learning. An experiment by Chang, Sung & Chen [5] compared the effectiveness of two computerized concept mapping environments (and one pencil-and-paper based). The two computerized concept mapping conditions were “construct-on-scaffold” and “construct-by-self.” For the construct-on-scaffold condition, an incomplete framework of a concept map was given in which some nodes and links were left as blanks for the students to fill in. This sort of framework was inspired by the notion of an “expert skeleton” map [6] in which the beginning of a concept map is set up by an expert and the rest of the map is to be completed by the learner. In the construct-byself condition, students freely constructed maps with “no aid.” However, in both conditions the program offered the aid of providing concept and relationship lists to add to the students’ maps. Hint and evaluation tools were also available to students. After controlling for the pre-test scores, differences in posttest scores were found to be significant between the three conditions. The construct-on-scaffold condition showed significantly more learning than the other two conditions, with no difference seen between the construct by self and the paper and pencil condition. This suggests that asking the students to create a concept map from a blank canvas was too much, even in a computerized environment with the additional help that the construct-by-self condition offered. We have seen across age groups, spanning to college aged students, similar evidence that without extensive training, novices struggle with creating concept maps without scaffolding [5, 7]. The task is seen as too time consuming and too cognitively demanding for students to accomplish; therefore, some form and amount of scaffolding appears to be necessary for effective concept mapping. Another prominent study of an ITS implementing the use of concept maps is that of Betty’s Brain [8-9]. The students’ objective was to teach a novice computer agent, Betty, about river ecosystems. The students helped structure the domain knowledge they were trying to convey through the use of concept maps and other visual tools [89]. Another way in which concept maps were utilized within the system was through Betty’s responses to student prompted questions; Betty explained her responses by highlighting her logical path through the student-created concept map. In a Betty’s Brain study [9] three different versions of the system were used, two of which included concept mapping as a learning technique. All three groups showed learning gains, but the two in which students were learning by teaching (with concept maps and other tools) performed better than the third group. Although these two studies highlight a few uses of concept maps in ITS’s, there lacks more comprehensive studies of the different functions concept maps can play. For example, researchers have not yet studied the difference in generating links versus nodes, nor the different roles that each play within concept maps. In addition, although we know that scaffolding is necessary, we cannot be sure how much assistance to offer and when such scaffolding should be removed as the student progresses through a domain. The proposed study will look at some of these unexplored questions.

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Our Proposal

The experiment we propose differs from previous work in how we treat the links within the concept maps. In the Chang, Sung, & Chen study [5], hints given were in the form of prompting students to complete the end node in a proposition by providing the linking words. This de-emphasized the importance of generating the links in a concept map. We propose an experiment to look deeper into the important role that links play within a concept map. Why might the links be worthy of such research? The simple answer is because of the relational properties that they contain. Relationships and structural information are important aspects in the transfer of knowledge [10], which is the end goal, and optimal outcome, of teaching students. Studies have repeatedly shown that novices do not do well at perceiving the relational or structural similarities between examples in different contexts [10-11]. Analogical reasoning, being able to apply relational properties between contexts, is an important aspect of learning, but students are not currently doing a very good job of this. It stands to reason, then, that students may benefit from having their attention directed more explicitly toward the relationships between concepts. In order to explore this topic more deeply, we propose an experiment in which students are provided with one concept map for each domain that they are taught in an ITS. We would instruct them to use the concept map as a note-taking device throughout the learning session with the system. In order to not overwhelm the students by asking them to create a map from scratch, different levels of completed and partially completed maps would be provided to the student. The presence of provided nodes and the presence of provided links would vary between being fully filled in and being completely blank. In other words, the experiment would be a 2 (providing all of the links or none of the links) x 2 (providing all of the nodes or none of the nodes) design. In the blank links, blank nodes condition the students will still be given the layout of the concept map (i.e., they will not be creating a map from scratch). The two conditions of most interest are the full links, blank nodes condition and the blank links, full nodes condition. In both of these conditions we have a medium level of scaffolding, and if links serve the important purpose that we believe they do, forcing the students to generate them in the blank links, full nodes condition will encourage larger learning gains than in the full links, blank nodes condition. This hypothesis is supported by studies which have shown that providing students with completely pre-constructed, filled in concept maps does not show as much learning gains as when the students are given the opportunity to generate the material themselves [12]. The four conditions in this experiment would also provide a wide coverage of different amounts of scaffolding which would allow us to more fully look at the assistance dilemma. The assistance dilemma is the issue of how much assistance to provide to students; if you give too much assistance they are bored and not stimulated, but if you give too little assistance they are confused and overwhelmed [13]. These conditions could be set up as within-subjects if each student is provided with one of each type of concept map for each section of a domain that an ITS covers. Further experiments would look at the effect of using a concept map which grows progressively along with the lesson and which reduces the scaffolding as the student

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progresses. If our research reveals that explicit learning of relational features increases learning gains, this would be progress toward more transferable learning and may affect ITS’s with and without concept maps.

References 1. Novak, J.D., Gowin, D.B.: Learning How to Learn. Cambridge University Press (1984) 2. Robinson, D.H., Skinner, C.H.: Why Graphic Organizers Facilitate Search Processes: Fewer Words or Computationally Efficient Indexing? Contemporary Educational Psychology 21, 166–180 (1996) 3. Winn, W.: Learning from Maps and Diagrams. Educational Psychology Review 3, 211– 247 (1991) 4. O’Donnell, A.M., Dansereau, D.F., Hall, R.H.: Knowledge Maps as Scaffolds for Cognitive Processing. Educational Psychology Review 14 (2002) 5. Chang, K.E., Sung, Y.T., Chen, S.F.: Learning through Computer-Based Concept Mapping with Scaffolding Aid. Journal of Computer Assisted Learning 17, 21–33 (2001) 6. Novak, J.D., Cañas, A.J.: The Theory Underlying Concept Maps and How to Construct and Use Them, vol. 284. Florida Institute for Human and Machine Cognition, Pensacola (2008) 7. Reader, W., Hammond, N.: Computer-Based Tools to Support Learning from Hypertext: Concept Mapping Tools and Beyond. Computers & Education 22, 99–106 (1994) 8. Biswas, G., Leelawong, K., Schwartz, D.L., Vye, N., Vanderbilt, T.T.A.G.A.: Learning by Teaching: A New Agent Paradigm for Educational Software. Applied Artificial Intelligence 19, 363–392 (2005) 9. Leelawong, K., Biswas, G.: Designing Learning by Teaching Agents: The Betty’s Brain System. International Journal of Artificial Intelligence in Education 18, 181–208 (2008) 10. Gentner, D.: Structure-Mapping: A Theoretical Framework for Analogy. Cognitive Science: A Multidisciplinary Journal 7, 155–170 (1983) 11. Gentner, D., Landers, R.: Analogical Reminding: A Good Match is Hard to Find. In: Proceedings of the International Conference on Systems, Man, and Cybernetics, Tucson, AZ (1985) 12. Cañas, A.J., Reiska, P., Novak, J.D.: Concept Mapping in e-Learning. STRIDE 122 (2010) 13. Koedinger, K.R., Pavlik Jr., P.I., McLaren, B.M., Aleven, V.: Is It Better to Give Than to Receive? The Assistance Dilemma as a Fundamental Unsolved Problem in the Cognitive Science of Learning and Instruction. In: Sloutsky, V., Love, B., McRae, K. (eds.) Proceedings of the 30th Conference of the Cognitive Science Society, Washington, D.C, pp. 2155– 2160 (2008)