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Draft Leveraging Data from Students’ Interactions with Intelligent Tutoring Systems Persistence and “Gaming the System” Using EDM techniques, researchers have identified concrete behaviors in online learning environments that reflect persistence or gaming the system. Persistence in an online learning environment or a game means spending more time on difficult problems as measured by indicators such as time on unsolved problems (controlling for ability), and number of restarts and revisits to unsolved problems (Shute & Ventura, in press). Time spent on problems has been positively linked to achievement outcomes in some studies, but not always. Thus, the nature of the learning environment and kinds of questions are an important factor. In contrast to behaviors associated with persistence, when students are gaming the system, they are “attempting to succeed in an educational environment by exploiting properties of the system rather than by learning the material and trying to use that knowledge to answer correctly” (Baker et al., 2006). Gaming behavior has been shown to be detrimental to learning (Baker et al., 2004), so it is important to detect and address. Detection of gaming behavior involves looking for “sustained and/or systematic guessing” and “repeated and rapid help requests” (Baker, Corbett, Roll, & Koedinger, 2008; Baker, D’Mello, Rodrigo, & Graesser, 2010). Help-Seeking Behavior Most cognitive and intelligent tutoring systems include scaffolds and help resources and track when students use these features. Data on help-seeking can include identification of the moment when a student asked for help, the amount of help provided, the average number of hints requested per problem; how much time and effort was invested when the student asked for help; the percentage of helped problems; whether help was requested before or after making an initial attempt or after entering the correct answer (Arroyo & Woolf, 2005). In one study on the Help Tutor, an enhanced version of Geometry Cognitive Tutor, students received metacognitive feedback on their help-seeking behaviors (Roll, Aleven, McLaren, & Koedinger, 2011). For example, if a student was “drilling down” to the bottom-level hint without reading the intermediate hints, the help tutor would suggest: “No need to hurry so much. Take your time and read the hint carefully. Consider trying to solve this step without another hint. You should be able to.” Students in the Help Tutor condition made fewer help seeking errors and learned more. Some example measures. Here we describe several illustrative examples of these types of measures. The first is a set of low-tech measures that simply use wait time. The Preschool Self- Regulation Assessment includes tasks such as the “Toy Wrap,” in which the child is asked not to peek while a toy is being wrapped, and the “Toy Wait,” in which the child must wait before touching the wrapped toy (Murray & Kochanska, 2002). In controlled laboratory settings, these kinds of performances are easily observable. The amount of time children can delay their gratification can be used as an indicator of executive functions and effortful control. The next set of measures includes approaches to examining students’ patterns of interactions with digital learning environments. Interaction patterns can demonstrate how tool use in an environment relates to mindsets, learning goals, learning strategies, and help-seeking. Examining these interaction patterns can provide insight into the relationships between a learner’s goals and behaviors. For example, many researchers are exploring how to leverage data from students’ interactions with intelligent tutoring systems, such as how they may “game the system” or seek help (see callout box for more information). 42
Draft Exhibit 10. A model developed by Shute and Ventura to measure indicators of conscientiousness within a digital learning environment, Newton’s Playground Source: Shute, V., & Ventura, M. (in press). Measuring and supporting learning in games stealth assessment. White paper for MIT series. MacArthur Foundation. Retrieved from http://myweb.fsu.edu/vshute/publications.html There are several examples in other types of digital learning environments. Exhibit 10 shows Shute and Ventura’s (in press) model to measure indicators of conscientiousness within a digital learning environment for physics, Newton’s Playground. Conati and colleagues, studying behaviors in a digital math learning environment, found that learning goals associated with selfreported conscientiousness, “learning math,” and “succeeding by myself” were associated with particular interaction patterns—using a “magnifying glass” to see a number’s factorization, asking advice often, and following advice often (Conati & Maclaren, 2009; Zhou & Conati, 2003). Winne and colleagues examined how a suite of study tools, gStudy (http://www.learningkit.sfu.ca/), can provide evidence of learning strategies. The software detects when learners create notes, which information is selected by the learner to address in the note, and how the learner classifies this information on the basis of the note template selected (Winne, 2006). 43
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Draft Contents Executive Summary ..
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Draft Executive Summary How can we
Page 9 and 10: Draft What Are Grit, Tenacity, and
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Page 13 and 14: Draft include those that help stude
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Page 17 and 18: Draft Conclusions and Recommendatio
Page 19 and 20: Draft 1. Introduction The test scor
Page 21 and 22: Draft schooling—mastering concept
Page 23 and 24: Draft implications for practices ar
Page 25 and 26: Draft Exhibit 1. Clusters of 21st-c
Page 27 and 28: Draft We must also consider that th
Page 29 and 30: Draft 2. What Are Grit, Tenacity, a
Page 31 and 32: Draft Resilience is another closely
Page 33 and 34: Draft For the purpose of this repor
Page 35 and 36: Draft Exhibit 4. Hypothesized model
Page 37 and 38: Draft • Timescale. Goals can be r
Page 39 and 40: Draft A Rigorous and Supportive Lea
Page 41 and 42: Draft Psychological Resources That
Page 43 and 44: Draft Effortful Control In Angela D
Page 45 and 46: Draft they should be taught. Howeve
Page 47 and 48: Draft can similarly impact whether
Page 49 and 50: Draft 3. Measuring Grit, Tenacity,
Page 51 and 52: Draft Why Measure Grit, Tenacity, a
Page 53 and 54: Draft measured. Further, these reso
Page 55 and 56: Draft Exhibit 8. Items from a self-
Page 57 and 58: Draft Exhibit 9. Example of Charact
Page 59: Draft Behavioral Task Performance
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Page 65 and 66: Draft Exhibit 12. Components of an
Page 67 and 68: Draft 4. Programs and Models for Le
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Page 71 and 72: Draft whether transferability may b
Page 73 and 74: Draft problems, manage time, monito
Page 75 and 76: Draft − Mental contrasting/implem
Page 77 and 78: Draft to get into college and gradu
Page 79 and 80: Draft KIPP (Knowledge is Power Prog
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Draft Conati, C., & Maclaren, H. (2
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