DOI: 10.21954/ou.rd.14414774
title: Impact of Predictive Learning Analytics on Course Awarding Gap - Supporting Data

description: <b>GENERAL INFORMATION</b><br>The dataset represents supporting data for the research findings of the paper accepted for AIED'21 conference: http://oro.open.ac.uk/76042/ <br><b>SHARING/ACCESS INFORMATION</b><br>Links to publications that cite or use the data: <i>Hlosta, Martin; Christothea, Herodotou; Miriam, Fernandez and Vaclav, Bayer Impact of Predictive Learning Analytics on Course Awarding Gap of Disadvantaged students in STEM. In: 22nd International Conference on Artificial Intelligence in Education, AIED 2021, Springer.</i><br>Was data derived from another source? <i>Yes - the data was derived from the internal OU data </i><br>Recommended citation for this dataset: <i>Hlosta, Martin; Christothea, Herodotou; Miriam, Fernandez and Vaclav, Bayer Impact of Predictive Learning Analytics on Course Awarding Gap of Disadvantaged students in STEM. In: 22nd International Conference on Artificial Intelligence in Education, AIED 2021, Springer.</i><br><br><b>DATA & FILE OVERVIEW</b><br>The dataset contains coefficients of a logistic and linear regression that was used to model 3 student outcomes in 3 STEM courses - 1) completion, 2) passing and 3) overall score. The results are split into four tabs<br>1. Regression BetasBets coefficients and the Standard Error for each variable student outcome , i.e. - completion: comp_B comp_SE - passing: pass_B pass_SE - overall score: score_B score_SE <br>2. LogReg Marginal Effectsthe marginal effect coefficients for the two dichotomous outcomes from the previous tab (completion and passing) More information about the marginal effects: https://www.statisticshowto.com/marginal-effects/<br>3. Reg_BAME - These are the regression coefficients reported in the in the first tab, for the same outcomes (i.e. completion/passing/overall score), but disaggregated by whether the student is identified as BAME or not. Note that the analysis does not contain the 'BAME' coefficients, because it would be constant<br>4. Red_IMDSimilarly as for BAME (point 3), these are regression coefficients disaggregated by IMD quintiles. IMD_Missing is a special category capturing the students without any IMD, i.e. international students.<br><b>Regression coefficient variables<br></b>The variables entering the regressions can be split into three categories and the intercept<br>(1) Student level - age - banded into age_&lt;21, age_[25-29], age_[30-39], age_&gt;60, age_MISSING (reference category: age_[21-24]) - gender - gender_F (reference category Gender_M) - an indicator of linked qualification - linked_qual (reference category: linked_qual =False) - declared disability - disability (reference category: disability=False) - caring responsibility carer_NO, carer_YES (reference category: carer_MISSING) - flag whether the student is new at the OU - is_new (reference category: is_new=False) - highest previous education - ed_NoFormal, ed_HE_Qual, ed_PostGrad (reference category: ed_A Level/Equivalent) - average previous score - discretised into prev_score_LOW, prev_score_MOD, prev_score_VERY_HIGH (avg.prev.score=MISSING, i.e. the student did not study any previous course) these are banded into 4 quartiles (LOW, MOD, HIGH, VERY_HIGH), independently for each course - i.e. the specific values of these thresholds vary for the courses, as they will usually have values of the average score. - number of other credits studied - banded as credits_other_[1-60], credits_other_&gt;=61 (reference category: credits_other=0) - number of previous attempts of the course - prev_attempt_=1, prev_attempt &gt;1 (reference category: prev_attempt_0) - IMD (Index of Multiple Deprivation) - banded into quintiles, i.e. imd_&lt;=20, imd_[21-40], imd_[61-80], imd_&gt;=81 imd_MISSING (reference category: imd_[41-60]) - whether the student is identified as BAME - BAME_YES (reference category: BAME_NO) - Membership in the intervention group - group_INT (reference category: group_INT=0) <br>(2) Teacher level - no. of students the teacher is responsible for - stud_in_group - avg. student pass rate in the previous years they were teaching - tut_pr_pass_LOW, tut_pr_pass_HIGH, tut_pr_pass_VERY_HIGH, tut_pr_pass_MISSING (reference category: tut_pr_pass_MOD) - these are banded into 4 quartiles (LOW, MOD, HIGH, VERY_HIGH), independently for each course - i.e. the specific values of these thresholds vary for the courses, as they will usually have different pass rates <br>(3) Course level - dummy variable encoded as - course_1, course_2 (reference category: course_3)<br>(4) intercept<br><br>