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Draft Interaction patterns also can be explored by tracking eye moments to see where learners focus attention during problem solving. Conati and Merten (2007), for example, used an eye-tracking device to examine metacognitive behaviors that are relevant for learning mathematical functions. The device provided information about how learners explored the stimuli, in this case the relationship between a function’s graph and equation. Examples of affective computing methods are growing. Mcquiggan, Lee, and Lester (2007) have used data mining techniques as well as physiological response data from a biofeedback apparatus that measures blood volume, pulse, and galvanic skin response to examine student frustration in an online learning environment, Crystal Island. Woolf, Burleson, Arroyo, Dragon, Cooper and Picard (2009) have been detecting affective indicators within an online tutoring system Wayang Outpost using four sensor systems, as illustrated in Exhibit 11. Sensors provide constant, parallel streams of data and are used with data mining techniques and self-report measures to examine frustration, motivation/flow, confidence, boredom, and fatigue. The MIT Media Lab Mood Meter (Hernandez, Hoque, & Picard, n.d.) is a device that can be used to detect emotion (smiles) among groups. The Mood Meter includes a camera and a laptop. The camera captures facial expressions, and software on the laptop extracts geometric properties on faces (like distance between corner lips and eyes) to provide a smile intensity score. While this type of tool may not be necessary in a small class of students, it could be useful for examining emotional responses in informal learning environments for large groups, like museums. Exhibit 11. Four parallel streams of affective sensors used while a student is engaged in Wayang Outpost, an online tutoring system Facial Expression Camera Posture Analysis Seat Pressure Mouse Wireless Skin Conductance Sensor Source: Woolf, B., Burleson, W., Arroyo, I., Dragon, T., Cooper, D., & Picard, R. (2009). Affect-aware tutors: Recognising and responding to student affect. International Journal of Learning Technology, 3/4, 129–164. The field of neuroscience also offers methods for insight into some of the psychological resources associated with grit, especially effortful control. Using neuroimaging techniques, such 44
Draft as fMRI, it is possible to examine which parts of the brain are active during times of anxiety or stress and the effects of some interventions. For example, Slagter, Davidson, and Lutz (2011) have investigated the effects of systematic mental training and meditation to enhance cognitive control and maintain optimal levels of arousal. Motivation was found to be associated with greater activation in multiple brain regions. Moreover, studies have reported functional and structural changes in the brain and improved performance of long-term practitioners of mindfulness and concentration meditation techniques that enhance attentional focus. These initial findings are promising evidence of the cognitive plasticity and malleability of brain functioning for processes related to grit. While it is impractical to use fMRI in the classroom (i.e., it is a prohibitively expensive, room-sized machine), Ed Dieterle and Ash Vasudeva of the Bill & Melinda Gates Foundation point out that researchers such as Jon Gabrieli and Richard Davidson are beginning to use multiple methods to explore how specific brain activity is correlated with other cognitive and affective indicators that are practical to measure in school settings. Methodological tradeoffs. Measures of behavioral task performance hold strong promise for deepening the field’s understanding of the interactions among the cognitive and affective processes underlying grit. They are minimally “fakable” (Kyllonen, 2005) and typically do not “cue the intentions” of the teacher or researcher (Shute & Ventura, in press). They do not require participants to have fully developed verbal skills or be able to articulate their own internal processes. Micro-level indicators also have the potential to be seamlessly integrated into a learning environment, and indicators can provide measures of behavior in real time, making it possible to examine and address dynamic changes in student understanding (e.g., how goals and affect change over time in an activity) (U.S. Department of Education Office of Educational Technology, 2013; Woolf et al., 2009). These methods are not, however, without their own set of challenges. It is important to recognize the immense effort that goes into interpreting the meaning of student log files, for example, before an intelligent tutor can be designed to “know” what a student’s behavior means and be able to offer appropriate scaffolds or feedback. The research into the design of these systems involves multiple observations and/or interviews of students interacting with the learning environment, achieving agreement among raters about how to interpret student behaviors and using these findings to design the programs that support student learning (e.g., Baker et al., 2008). Another issue with some micro-level indicators is that the approaches for gathering information can be intrusive or impractical for use in school settings. For instance, eye tracking devices can be distracting, difficult for people with heavy eyelashes or glasses, and compromised by movements from participants (Conati & Merten, 2007). Machines such as fMRI, and devices that measure EEG and skin conductance may not be practical for use in the classroom. Finally, many of these types of measures are dependent on the use of highly constrained tasks in digital learning environments, which may be difficult to translate into use in the classroom or informal learning environment. 45
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Draft Contents Executive Summary ..
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Draft Executive Summary How can we
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Draft What Are Grit, Tenacity, and
Page 11 and 12: Draft One important distinction tha
Page 13 and 14: Draft include those that help stude
Page 15 and 16: Draft Recommendation 2b: Technology
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 and 60: Draft Behavioral Task Performance
Page 61: Draft Exhibit 10. A model developed
Page 65 and 66: Draft Exhibit 12. Components of an
Page 67 and 68: Draft 4. Programs and Models for Le
Page 69 and 70: Draft Exhibit 13. Summary of progra
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
Page 81 and 82: Draft Hammond (2002) discusses ten
Page 83 and 84: Draft receive year-round support an
Page 85 and 86: Draft 5. Digital Learning Environme
Page 87 and 88: Draft Personalized Learning at Scal
Page 89 and 90: Draft • Digital learning environm
Page 91 and 92: Draft • Digital learning environm
Page 93 and 94: Draft 5. Conclusions and Recommenda
Page 95 and 96: Draft Research-Based Best Practices
Page 97 and 98: Draft supported to employ strategie
Page 99 and 100: Draft Recommendation 2b: Technology
Page 101 and 102: Draft in which students feel comfor
Page 103 and 104: Draft Limitations include short sch
Page 105 and 106: Draft Parents can also support chil
Page 107 and 108: Draft developing learning trajector
Page 109 and 110: Draft There is still much important
Page 111 and 112: Draft methodology, implementation r
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Draft References Note: References m
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Draft Conati, C., & Maclaren, H. (2
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Draft Mislevy, R. J., Steinberg, L.
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Draft Slagter, H. A., Davidson, R.
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