Difference between revisions of "Explainee moves"
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<h2>Substantive segment</h2> | <h2>Substantive segment</h2> | ||
A substantive segment is defined as a meaningful contribution to an ongoing activity, such as problem solving, or a relevant response to the Tutor’s explanations <!--\parencite{chi2008observing}--><ref name="chi2008observing"/>. For example, to the Tutor explanation shown below the Tutee’s response would be coded as a substantive one: | We reserve this move mostly for mathematical practices. A substantive segment/response is defined as a meaningful contribution to an ongoing activity, such as problem solving, or a relevant response to the Tutor’s explanations <!--\parencite{chi2008observing}--><ref name="chi2008observing">Chi, M. T., Roy, M., & Hausmann, R. G. (2008). Observing tutorial dialogues collaboratively: Insights about human tutoring effectiveness from vicarious learning. Cognitive science, 32(2), 301-341</ref>. For example, to the Tutor explanation shown below the Tutee’s response would be coded as a substantive one: | ||
-'''Tutor:''' See this equation is true for constant acceleration. Now the acceleration is constant here. Forces are not changing on the weight so the acceleration is constant. | -'''Tutor:''' See this equation is true for constant acceleration. Now the acceleration is constant here. Forces are not changing on the weight so the acceleration is constant. | ||
-'''Tutee:''' '''''The initial velocity is zero then.''''' ---> '''''Substantive response''''' | -'''Tutee:''' '''''The initial velocity is zero then.''''' ---> '''''Substantive response''''' | ||
Latest revision as of 11:46, 28 September 2022
Deep follow-up
a deep follow-up, which is an elaborative inference that extends what the tutor said .[1] Examples of deep follow-ups are as follows:
Context: Each of the valves consists of flaps of tissue that open as blood is pumped out of the ventricles. -Tutor:“blood actually flows out through there.” -Student: This contracts like a balloon and forces this venous blood up here. ---> deep follow-up
Context: At first the molecules of sugar are more concentrated in and around the sugar cube and less concentrated in the water farther from the cube. -Tutor:“This cube of sugar is disintegrating, breaking apart, expanding into all spaces . . .” -Student:Until, until equilibrium is accomplished. ---> deep follow-up
shallow follow-up
a shallow follow-up is an elaborative paraphrase of what the tutor said. .[1] Examples of shallow follow-ups are as follows:
Text sentence #1: Human life depends on the distribution of oxygen, hormones, and nutrients to cells in all parts of the body and on the removal of carbon dioxide and other wastes. -Tutor: “Basically, what we are talking about is the circulatory system is an exchange of materials.” -Student: You take out the waste and you put in the nutrients. ----> shallow follow-up
Text sentence #16: Each of the valves consists of flaps of tissue that open as blood is pumped out of the ventricles. -Tutor: “OK. So opening and closing, what would that do?” -Student: It would allow the blood to enter like from the atrium without it falling straight through. ----> shallow follow-up
Substantive segment
We reserve this move mostly for mathematical practices. A substantive segment/response is defined as a meaningful contribution to an ongoing activity, such as problem solving, or a relevant response to the Tutor’s explanations [2]. For example, to the Tutor explanation shown below the Tutee’s response would be coded as a substantive one:
-Tutor: See this equation is true for constant acceleration. Now the acceleration is constant here. Forces are not changing on the weight so the acceleration is constant.
-Tutee: The initial velocity is zero then. ---> Substantive response
Notes
- ↑ 1.0 1.1 Chi, M. T., Siler, S. A., Jeong, H., Yamauchi, T., & Hausmann, R. G. (2001). Learning from human tutoring. Cognitive science, 25(4), 471-533.
- ↑ Chi, M. T., Roy, M., & Hausmann, R. G. (2008). Observing tutorial dialogues collaboratively: Insights about human tutoring effectiveness from vicarious learning. Cognitive science, 32(2), 301-341