Fernando Carnauba Assignment #5. Rmd and HTML files. #103
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Dec 19, 2019
| 1) Students that ask for any hints. The first behavior teachers should pay attention to is students that ask for any hints. This category includes 42% of the population, and among these, 48% should receive advice. | ||
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| 2) Students whose percentage of prior correct answers is below .6 (among students that asked for hints).Among those students that asked for hints, paying attention to their prior correct answer percentage provides adittional information for teachers about which students are likely to benefit from receiving advice. Among students with prior correct answer rate below .6 AND who have asked for at least one hint, 60% should receive advice. This "dual" category ("at leas 1 hint" + "correct answer rate below .6") includes 37.7% of the total students who should receive advice ( = .22*.6/.35) | ||
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| #Upload new data | ||
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| D2 <- read.csv("~/R/HUDK/assignment5/intelligent_tutor_new.csv") | ||
| D2$advice <- ifelse(D2$score == 1, 0, 1) # Anna told me that for this variable, students coded as "1" should not receive advice, while students coded "0" should (I find this actually very weired since everyone in the dataset has score =1) |
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I agree, for the pure sake of value-label, that "advice = 1" means receive advice, and "advice=0" means no advice. But I am confused about the two different ways you use to define the variable "advice". I would feel the first one D2$advice <- ifelse(.3 <D2$score & D2$score <= .6, 1, 0) makes better sense for me, although the results are exactly the same since the score in D2 is a distinguishable variable (which is weird due to the dataset itself).
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| It seems that the new data is very different from our training data, in the sense that in the training data we had 35% of students needing advice, while in the new data 0% of students needed advice. On the other hand, student inputs seem to be more stable (prior_percent_correct, hints, prior_prob_count), leading to a percentage of predicted "in-need-of-adcive-students" of 27%. Overall, our recommendation was "correct" for the 73% of students for whom we did not recommend advice. This is not that much of a good model though, when we compare to simply recommending the "most usual" state (no advice) - if we had done that, we would have got 100% correct recommendations. | ||
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Nice work! This analysis is interesting and reasonable.
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Fernando Carnauba Assignment #5. Rmd and HTML files.