Impact of Predictive Learning Analytics on Course Awarding Gap - Supporting Data

GENERAL INFORMATIONThe dataset represents supporting data for the research findings of the paper accepted for AIED'21 conference: http://oro.open.ac.uk/76042/ SHARING/ACCESS INFORMATIONLinks to publications that cite or use the data: 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.Was data derived from another source? Yes - the data was derived from the internal OU data Recommended citation for this dataset: 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.DATA & FILE OVERVIEWThe 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 tabs1. Regression BetasBets coefficients and the Standard Error for each variable student outcome , i.e. - completion: comp_Bcomp_SE- passing: pass_Bpass_SE- overall score: score_Bscore_SE 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/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 constant4. 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.Regression coefficient variablesThe variables entering the regressions can be split into three categories and the intercept(1) Student level- age - banded into age_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_>=61 (reference category: credits_other=0)- number of previous attempts of the course - prev_attempt_=1, prev_attempt >1 (reference category: prev_attempt_0)- IMD (Index of Multiple Deprivation) - banded into quintiles, i.e. imd_=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) (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 (3) Course level - dummy variable encoded as - course_1, course_2 (reference category: course_3)(4) intercept