DESCRIPCIÓN DE MODELOS

AutoTutor (Person et al., 2001; Graesser, Rus, D’Mello, & Jackson, 2008; Graesser, 2011) teaches topics in computer literacy such as Hardware, Operating Systems and the Internet by having a conversation with students. AutoTutor requires students to provide lengthy explanations for How,

Why and What-if type of questions. This approach encourages students to articulate lengthier answers that exhibit deeper reasoning instead of short answers, which may lead to shallow knowledge. The natural language processing components of the tutor are based on Latent Semantic Analysis (LSA) (Landauer & Dumais, 1997).

Several systems have been evolved from the original AutoTutor to cover different domains including biology (Graesser, D’Mello, & Person, 2009) (GuruTutor), research ethics (Hu & Graesser, 2004) (HURA Advisor), critical thinking in science (Millis et al., 2011), physics (Graesser, Franceschetti, Gholson, & Craig, 2011) and self-regulated learning (Azevedo, Johnson, Chauncey, & Burkett, 2010). Here we focus on some of the important details of the AutoTutor teaching computer literacy.

Figure 2.17 presents the interface of Why2-AutoTutor (VanLehn et al., 2007), an ITS similar to AutoTutor that teaches qualitative physics. The animated agent that acts as a dialogue partner with the student appears on the top-left corner (Figure 2.17). The agent delivers AutoTutor’s dialogue moves with synthesized speech, intonations, facial expressions and gestures. The problem that the student receives is both spoken by AutoTutor and is printed at the top of the screen. Questions in the dialogue are generated systematically from a curriculum script, a module discussed below. The system interaction involves a multi-turn, mixed-initiative dialogue with the student. When the turns of both the learner and AutoTutor are considered, it takes 10 to 20 conversational turns to answer a single question or solve a problem from the curriculum script.

The curriculum script is a sequence of topic formats, each of which contains a main focal question, and a pre-specified ideal complete answer. The ideal complete answer consists of several sub-answers, called aspects. Each aspect has the following information: (i) a list of expected bad

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answers corresponding to misconceptions that need correction (with splicing/correcting moves); (ii) lists of prompts and hints that can be used to encourage the learner to contribute information and (iii) elaboration and summary moves that can be used to provide the learner with additional or summarizing information. All these moves are pre-specified.

The tutoring session starts with a brief introduction and then a question from the curriculum script is presented to the student. AutoTutor evaluates the student’s answer against all the aspects of the ideal complete answer, and the anticipated bad answers. The system provides immediate feedback based on the student’s answer, and then executes dialogue moves that encourages the learner to contribute information until all answer aspects are sufficiently covered.

Figure 2.17: User interface of Why2-AutoTutor (VanLehn et al., 2007)

Selecting these dialogue moves is based on (i) the quality of the student’s assertion in the preceding turn; (ii) global parameters like student ability, verbosity and initiative; and (iii) the extent to which the good answer aspects have already been covered. A set of fuzzy production rules determines the category of the dialogue move to be selected. The content of the dialogue move is computed by an algorithm that selects the next good answer aspect to focus on. Currently

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TUTOR-1: Alright, let's go on. You want to take photos and send them over the Internet. Consider this problem. What hardware do you need to take photos and send them over the Internet?

STUDENT-1: First of all you need to have access to the internet. TUTOR-2: What else?

STUDENT-2: a camera

TUTOR-3: Yeah. For what type of connection do you need a network card? STUDENT-3: network card

TUTOR-4: Okay. Now, if you have access to the Internet through a network card, then your connection is… STUDENT-4: I don't know.

TUTOR-5: Is direct. Okay. A network card is needed if you have a direct connection to the Internet. Okay. How does the user get hooked up to the internet?

STUDENT-5: by a plug.

TUTOR-6: Once again, a network card is needed if you have a direct connection to the Internet. Okay, what does a modem do?

STUDENT-6: connects network card and internet

TUTOR-7: Okay. Try this. A modem is required when there is… STUDENT-7: a connection

TUTOR-8: a dial up connection. ..

TUTOR-30: Let’s review. To send your photos on the internet, you need either a digital camera or a regular camera to take the photos. If you use a regular camera, you need a scanner to scan them onto a computer disk. If you have a direct connection to the Internet, then you need a network card. A modem is needed if you have a dial up connection.

Figure 2.18: An Example Tutorial Dialogue between AutoTutor and a learner (Graesser, VanLehn, Rosé, Jordan, & Harter, 2001)

12 dialogue moves are possible: Pump, Hint, Splice, Prompt, Prompt Response, Elaboration, Summary, and five forms of immediate short-feedback (positive, positive-neutral, neutral, negative-neutral, and negative).

Figure 2.18 shows a dialogue between a college student and AutoTutor. Prior to the first question in the Figure 2.18 (TUTOR-1), the student has attempted to answer 6 previous questions about the internet. Tutor-2 is an example of a Pump, which is used to elicit more information from the student. Prompts are used to encourage the learner to produce a single word as shown in Tutor- 3 and Tutor-4. Assertions are given in Tutor-5 and Tutor-6.

During the discussion of a topic, the system needs to keep track of both the good answer aspects that have been covered and the dialogue moves that have been generated. In AutoTutor 1.1, LSA Topic Coverage metric is used to decide whether each good answer aspect for a topic has been covered. LSA computes the extent to which the various tutor and student turns cover each good answer aspect associated with a particular topic. The Topic Coverage metric varies from 0 to

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1 and gets updated for each good answer aspect with each tutor and student turn. When a pre- specified threshold is met or exceeded, then good answer aspect is considered to be covered. A topic is finished when all of aspects have coverage values that meet or exceed the threshold.

One of the other important decisions is to select which aspect to focus on next. Different versions of AutoTutor employ different strategies for this selection. AutoTutor 1.1 uses the idea of zone of proximal development (Vygotsky, 1978) to select the good answer aspect to focus on next. It selects the aspect that has the highest subthreshold coverage score. This selection mechanism attempts to build on the fringes of what the student knows. AutoTutor 2.0 uses two additional features: discourse coherence (selecting the aspect that is most similar to the previous one that was covered) and pivotal aspects (selecting an aspect that has the greatest content overlap). In addition, AutoTutor 2.0 uses discourse patterns that organize dialogue moves in terms of their progressive specificity. Hints are less specific than prompts, and prompts are less specific than elaborations. Thus, AutoTutor 2.0 cycles through a Hint-Prompt-Elaboration pattern until the student articulates an aspect. The other dialogue moves (e.g., short feedback and summaries) are controlled by the fuzzy production rules available in AutoTutor 1.1.

The system has undergone several evaluation studies. They reveal that students interacting with AutoTutor repeatedly learnt significantly more than students who study a text book for a similar amount of time (Person et al., 2001; Graesser et al., 2003; Graesser et al., 2004; Graesser et al., 2008; Graesser, 2011).