We wish to extend our study of using student modelling in an ITS to determine how to tutor in a multimedia tutoring system. For example, can recent student performance be used to determine if a topic should be presented textually or graphically? Are there passive and active graphical tutoring tactics? Integrating current ITS technology with multimedia education systems will allow the full potential of multimedia to be exploited in support of learning.
The most striking phenomenon observed from these transcripts is our tutors' frequent use of hints (Hume et al., 1993, 1996a). We subsequently identified all of our tutors' tactics and positioned then on a passive to active continuum (Hume et al., 1995). Hints engage students in the greatest degree of cognitive activity. A didactic explanation allows the student to be a passive participant. A human tutor constantly monitors the progress of a student. This activity is referred to as student modelling in an ITS. We have identified how a human tutor uses the equivalent of a student model to select tactics (Hume et al., 1996b). Our tutors tend to employ active tactics if they believe that student can make use of them; they resort to passive tactics when the student performs poorly.
We wish to generalize our findings; we are aware that our domain is narrow in many ways. Our students are older and more motivated than most subjects of ITS experiments. The solutions to CST problems require that the student understand qualitative, causal relationships; there is little quantitative manipulation required. The primary form of communication is natural language via the keyboard and screen. We hope to learn (1) how the hinting phenomenon can be implemented in a multimedia environment, (2) how different tactics in a multimedia environment require different degrees of cognitive activity of the student and (3) the role of student modelling in a multimedia tutoring environment.
This paper will (1) illustrate key aspects of our work with CST, (2) introduce a domain that will be used for a new multimedia ITS and (3) outline an argument for why traditional ITS technology should be incorporated in multimedia education.
We have identified two categories of hints: point-to hints (PT-Hints) and convey-information hints (CI-Hints). A CI-Hint explicitly conveys information in the form of an explanation or summary and is followed up with a question. A PT-Hint alludes to information presumed to be available to the student. This information does not contain any part of a desired answer but provides information that should enable the student to proceed.
We have further identified three other tactics: explanations, summaries and directed line of reasoning (DLR). A DLR is an interactive dialogue in which the tutor prompts the student in a stepwise manner to arrive at a solution to a problem. The intention of the tutor is to help the student reason about the problem at hand. Thus, the tactics we have identified, in order of the passive to active participation they require of the student, are: explanation, summary, DLR, CI-Hint and PT- Hint.
After interviews with our tutors, we have concluded that they use a very coarse scheme to evaluate their students. Specifically, they maintain a local and global assessment. A local assessment is the tutor's assessment of how the student is responding during a short segment of the tutoring session. This is based on qualitative characteristics of the dialogue. As an example, a student who understands that an utterance is a hint can be evaluated positively by the tutor even if the student's answer is incorrect. The tutor's global assessment of the student is a measure of the student's behavior throughout the entire tutoring session.
We have observed that our tutors use the following rules to determine when to hint (Hume et al., 1996c):
Several class periods are spent on mathematical and computer related topics. Eventually, students are expected to trace the execution of a pseudo machine code. After completion of such traces, students are asked to state what values reside in various registers and memory locations. Most students need to be tutored to reach these goals. This is an ideal domain for multimedia tutoring because:
Machine Address Code 00 1A 01 A0 02 1B 03 A1 04 5C 05 AB 06 3C 07 A2 08 C0 09 00 (later in memory) A0 FA A1 37Figure 1. A sample pseudo language program.
1 - Place value found in memory location F1F2 into register F3 3 - Place value found in register F1 into memory location F1F2. 5 - Add, in two's complement, the contents of registers F2 and F3. Then, place result in register F1. C - Halt execution.The sequence of digits placed in the IR and the ensuing action from the program in Figure 1 is:
1AA0 Places FA (from location A0) into Register A. 1BA1 Places 37 (from location A1) into Register B 5CAB Add (two's complement) FA and 37 (from Registers A, B). Places result, 31, in Register C. 3CA2 Places 31 (from Register C) into location A2 C000 Halt.This small program illustrates the relationship between the underlying concepts, isolated operations and higher level tasks required of the student. For example, an often used isolated skill is the conversion of numbers between hex and binary format. The student also must understand the underlying concepts of place numbering systems (i.e., places represent the base raised to successive powers, the base determines the number of digits, any number can be represented in any base). Animation or traditional means (i.e., the scrolling of text) can be employed to tutor either the isolated operation or the underlying concepts.
An example of a higher level task is the '5' op code from the above example. Values are retrieved from registers A and B. The two numbers, interpreted as two's complement numbers, are added. The result, converted back to hex, is placed in a register C. This one higher level task requires that the student has mastered many isolated operations and understands many underlying concepts.
We are beginning to design a protocol for tutor-student interaction. It is important to derive a system where errors can be identified and assumptions about the cause of errors can be verified. We then hope, through pictures and animation, to simulate the hinting activity that is natural with human tutors. One possibility is an interactive multimedia environment where the student is prompted to complete pictures that illustrate a concept.
How would a sophisticated tutor (human or ITS) use such multimedia resources? How would the tutor promote the kind of active learning that we are attempting to get CST to encourage? What is a visual hint? We can only suggest possible answers to these questions at this time. Much research will be needed to confirm the validity of such answers.
Both PT-Hints and CI-Hints convey some information to the user without actually providing the answer being sought. A visual hint might consist of an image (still or animated) delivered without accompanying labels or other textual information (whether printed to the screen or delivered by voice). The degree of cognitive activity required of the student can then be varied by providing labels and/or text in a graded fashion. A fully labeled and annotated figure would then represent the least active tutoring tactic (the equivalent of an explanation in CST).
An ITS able to tutor students in this manner could be used to address a number of significant issues. Does the simultaneous use of two sensory modalities assist in the acquisition of information? For example, does an auditory explanation accompanying a graphical image (auditory and vision) work better than a written explanation accompanying a graphical image (vision only)? Can iconic representations of processes or data flow paths serve to trigger the desired cognitive activity? Can an icon be a PT-Hint?
Hume, G., Michael, J., Rovick, A., & Evens, M. (1993). The use of hints as a tutorial tactic. Proceedings of the 15th Annual Conference of the Cognitive Science Society (pp. 563-568). Boulder, CO. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.
Hume, G., Michael, J., Rovick, A., & Evens, M. (1995). Controlling active learning: how tutors decide when to generate hints. Proceedings of the 8th Florida Artificial Intelligence Research Symposium (pp. 157-161). Melbourne Beach, FL.
Hume, G., Michael, J., Rovick, A., & Evens, M. (1996a). Hinting as a tactic in one-on-one tutoring. The Journal of Learning Sciences (5(1), 23-47). Hillsdale, NJ.: Lawrence Erlbaum Associates, Inc.
Hume, G., Michael, J., Rovick, A., & Evens, M. (1996b). Student responses and follow up tactics in an ITS. To appear in the Proceedings of the 9th Florida Artificial Intelligence Research Symposium. Key West, FL.
Hume, G., Michael, J., Rovick, A., & Evens, M. (1996c) The use of hints by human and computer tutors: the consequences of the tutoring protocol. To appear in the Proceedings of the 2nd International Conference on the Learning Sciences. Evanston, IL.
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