Penn Center for Learning Analytics Wiki - User contributions [en]

Gender: Male/Female

2022-06-03T17:24:05Z

Joyce:

Kai et al. (2017) [https://www.upenn.edu/learninganalytics/ryanbaker/DLRN-eVersity.pdf pdf]
* Models predicting student retention in an online college program
* J48 decision trees achieved significantly lower Kappa but higher AUC for male students than female students
* JRip decision rules achieved much lower Kappa and AUC for male students than female students

Christie et al. (2019) [https://files.eric.ed.gov/fulltext/ED599217.pdf pdf]
* Models predicting student's high school dropout
* The decision trees showed very minor differences in AUC between female and male students

Hu and Rangwala (2020) [https://files.eric.ed.gov/fulltext/ED608050.pdf pdf]
* Models predicting if a college student will fail in a course
* Multiple cooperative classifier model (MCCM) model was the best at reducing bias, or discrimination against male students, performing particularly better for Psychology course.
* Other models (Logistic Regression and Rawlsian Fairness) performed far worse for male students, performing particularly worse in Computer Science and Electrical Engineering.

Anderson et al. (2019) [https://www.upenn.edu/learninganalytics/ryanbaker/EDM2019_paper56.pdf pdf]
* Models predicting six-year college graduation
* False negatives rates were greater for male students than female students when SVM, Logistic Regression, and SGD were used

Gardner, Brooks and Baker (2019) [https://www.upenn.edu/learninganalytics/ryanbaker/LAK_PAPER97_CAMERA.pdf pdf]
* Model predicting MOOC dropout, specifically through slicing analysis
* Some algorithms studied performed worse for female students than male students, particularly in courses with 45% or less male presence

Riazy et al. (2020) [https://www.scitepress.org/Papers/2020/93241/93241.pdf pdf]
* Model predicting course outcome
* Marginal differences were found for prediction quality and in overall proportion of predicted pass between groups
* Inconsistent in direction between algorithms.

Lee and Kizilcec (2020) [https://arxiv.org/pdf/2007.00088.pdf pdf]
* Models predicting college success (or median grade or above)
* Random forest algorithms performed significantly worse for male students than female students
* The fairness of the model, namely demographic parity and equality of opportunity, as well as its accuracy, improved after correcting the threshold values from 0.5 to group-specific values

Yu et al. (2020) [https://files.eric.ed.gov/fulltext/ED608066.pdf pdf]
* Model predicting undergraduate short-term (course grades) and long-term (average GPA) success
* Female students were inaccurately predicted to achieve greater short-term and long-term success than male students.
* The fairness of models improved when a combination of institutional and click data was used in the model

Yu et al. (2021) [https://dl.acm.org/doi/pdf/10.1145/3430895.3460139 pdf]
* Models predicting college dropout for students in residential and fully online program
* Whether the socio-demographic information was included or not, the model showed worse true negative rates and worse accuracy for male students
* The model showed better recall for male students, especially for those studying in person
* The difference in recall and true negative rates were lower, and thus fairer, for male students studying online if their socio-demographic information was not included in the model

Riazy et al. (2020) [https://www.scitepress.org/Papers/2020/93241/93241.pdf pdf]
* Models predicting course outcome of students in a virtual learning environment (VLE)
* More male students were predicted to pass the course than female students, but this overestimation was fairly small and not consistent across different algorithms
* Among the algorithms, Naive Bayes had the lowest normalized mutual information value and the highest ABROCA value

Bridgeman et al. (2009)
[https://www.researchgate.net/publication/242203403_Considering_Fairness_and_Validity_in_Evaluating_Automated_Scoring pdf]

* Automated scoring models for evaluating English essays, or e-rater
* E-Rater system performed comparably accurately for male and female students when assessing their 11th grade essays

Bridgeman et al. (2012) [https://www.tandfonline.com/doi/pdf/10.1080/08957347.2012.635502?needAccess=true pdf]
* A later version of automated scoring models for evaluating English essays, or e-rater
* E-Rater system correlated comparably well with human rater when assessing TOEFL and GRE essays written by male and female students

Verdugo et al. (2022) [https://https://www.researchgate.net/profile/Jonathan-Vasquez-Verdugo/publication/359176069_FairEd_A_Systematic_Fairness_Analysis_Approach_Applied_in_a_Higher_Educational_Context/links/622ba9e89f7b324634245afa/FairEd-A-Systematic-Fairness-Analysis-Approach-Applied-in-a-Higher-Educational-Context.pdf pdf]
* An algorithm predicting dropout from university after the first year
* Several algorithms achieved better AUC for male than female students; results were mixed for F1.

Zhang et al. (in press)
* Detecting student use of self-regulated learning (SRL) in mathematical problem-solving process
* For each SRL-related detector, relatively small differences in AUC were observed across gender groups.
* No gender group consistently had best-performing detectors

Black/African-American Learners in North America

2022-06-03T17:23:08Z

Joyce:

Kai et al. (2017) [https://www.upenn.edu/learninganalytics/ryanbaker/DLRN-eVersity.pdf pdf]
* Models predicting student retention in an online college program
* J48 decision trees achieved much lower Kappa and AUC for Black students than White students
* JRip decision rules achieved almost identical Kappa and AUC for Black students and White students

Hu and Rangwala (2020) [https://files.eric.ed.gov/fulltext/ED608050.pdf pdf]
* Models predicting if a college student will fail in a course
* Multiple cooperative classifier model (MCCM) model was the best at reducing bias, or discrimination against African-American students, while other models (particularly Logistic Regression and Rawlsian Fairness) performed far worse
* The level of bias was inconsistent across courses, with MCCM prediction showing the least bias for Psychology and the greatest bias for Computer Science

Christie et al. (2019) [https://files.eric.ed.gov/fulltext/ED599217.pdf pdf]
* Models predicting student's high school dropout
* The decision trees showed little difference in AUC among White, Black, Hispanic, Asian, American Indian and Alaska Native, and Native Hawaiian and Pacific Islander.

Lee and Kizilcec (2020) [https://arxiv.org/pdf/2007.00088.pdf pdf]
* Models predicting college success (or median grade or above)
* Random forest algorithms performed significantly worse for underrepresented minority students (URM; American Indian, Black, Hawaiian or Pacific Islander, Hispanic, and Multicultural) than non-URM students (White and Asian)
* The fairness of the model, namely demographic parity and equality of opportunity, as well as its accuracy, improved after correcting the threshold values from 0.5 to group-specific values

Yu et al. (2020) [https://files.eric.ed.gov/fulltext/ED608066.pdf pdf]
* Model predicting undergraduate short-term (course grades) and long-term (average GPA) success
* Black students were inaccurately predicted to perform worse for both short-term and long-term
* The fairness of models improved when either click or a combination of click and survey data, and not institutional data, was included in the model

Yu et al. (2021) [https://dl.acm.org/doi/pdf/10.1145/3430895.3460139 pdf]
* Models predicting college dropout for students in residential and fully online program
* Whether the socio-demographic information was included or not, the model showed worse true negative rates for students who are underrepresented minority (URM; or not White or Asian), and worse accuracy if URM students are studying in person
* The model showed better recall for URM students, whether they were in residential or online program

Ramineni & Williamson (2018) [https://files.eric.ed.gov/fulltext/EJ1202928.pdf pdf]
* Revised automated scoring engine for assessing GRE essay
* E-rater gave African American test-takers significantly lower scores than human raters when assessing their written responses to argument prompts
* The shorter essays written by African American test-takers were more likely to receive lower scores as showing weakness in content and organization

Bridgeman et al. (2009) [https://www.researchgate.net/publication/242203403_Considering_Fairness_and_Validity_in_Evaluating_Automated_Scoring pdf]
* Automated scoring models for evaluating English essays, or e-rater
* The score difference between human rater and e-rater was significantly smaller for 11th grade essays written by White and African American students

Bridgeman et al. (2012) [https://www.tandfonline.com/doi/pdf/10.1080/08957347.2012.635502 pdf]
* A later version of automated scoring models for evaluating English essays, or e-rater
* E-rater gave significantly lower score than human rater when assessing African-American students’ written responses to issue prompt in GRE

Jiang & Pardos (2021) [https://dl.acm.org/doi/pdf/10.1145/3461702.3462623 pdf]
* Predicting university course grades using LSTM
* Roughly equal accuracy across racial groups
* Slightly better accuracy (~1%) across racial groups when including race in model

Zhang et al. (in press)
* Detecting student use of self-regulated learning (SRL) in mathematical problem-solving process
* For each SRL-related detector, relatively small differences in AUC were observed across racial/ethnic groups.
* No racial/ethnic group consistently had best-performing detectors

Black/African-American Learners in North America

2022-06-03T17:22:50Z

Joyce:

Kai et al. (2017) [https://www.upenn.edu/learninganalytics/ryanbaker/DLRN-eVersity.pdf pdf]
* Models predicting student retention in an online college program
* J48 decision trees achieved much lower Kappa and AUC for Black students than White students
* JRip decision rules achieved almost identical Kappa and AUC for Black students and White students

Hu and Rangwala (2020) [https://files.eric.ed.gov/fulltext/ED608050.pdf pdf]
* Models predicting if a college student will fail in a course
* Multiple cooperative classifier model (MCCM) model was the best at reducing bias, or discrimination against African-American students, while other models (particularly Logistic Regression and Rawlsian Fairness) performed far worse
* The level of bias was inconsistent across courses, with MCCM prediction showing the least bias for Psychology and the greatest bias for Computer Science

Christie et al. (2019) [https://files.eric.ed.gov/fulltext/ED599217.pdf pdf]
* Models predicting student's high school dropout
* The decision trees showed little difference in AUC among White, Black, Hispanic, Asian, American Indian and Alaska Native, and Native Hawaiian and Pacific Islander.

Lee and Kizilcec (2020) [https://arxiv.org/pdf/2007.00088.pdf pdf]
* Models predicting college success (or median grade or above)
* Random forest algorithms performed significantly worse for underrepresented minority students (URM; American Indian, Black, Hawaiian or Pacific Islander, Hispanic, and Multicultural) than non-URM students (White and Asian)
* The fairness of the model, namely demographic parity and equality of opportunity, as well as its accuracy, improved after correcting the threshold values from 0.5 to group-specific values

Yu et al. (2020) [https://files.eric.ed.gov/fulltext/ED608066.pdf pdf]
* Model predicting undergraduate short-term (course grades) and long-term (average GPA) success
* Black students were inaccurately predicted to perform worse for both short-term and long-term
* The fairness of models improved when either click or a combination of click and survey data, and not institutional data, was included in the model

Yu et al. (2021) [https://dl.acm.org/doi/pdf/10.1145/3430895.3460139 pdf]
* Models predicting college dropout for students in residential and fully online program
* Whether the socio-demographic information was included or not, the model showed worse true negative rates for students who are underrepresented minority (URM; or not White or Asian), and worse accuracy if URM students are studying in person
* The model showed better recall for URM students, whether they were in residential or online program

Ramineni & Williamson (2018) [https://files.eric.ed.gov/fulltext/EJ1202928.pdf pdf]
* Revised automated scoring engine for assessing GRE essay
* E-rater gave African American test-takers significantly lower scores than human raters when assessing their written responses to argument prompts
* The shorter essays written by African American test-takers were more likely to receive lower scores as showing weakness in content and organization

Bridgeman et al. (2009) [https://www.researchgate.net/publication/242203403_Considering_Fairness_and_Validity_in_Evaluating_Automated_Scoring pdf]
* Automated scoring models for evaluating English essays, or e-rater
* The score difference between human rater and e-rater was significantly smaller for 11th grade essays written by White and African American students

Bridgeman et al. (2012) [https://www.tandfonline.com/doi/pdf/10.1080/08957347.2012.635502 pdf]
* A later version of automated scoring models for evaluating English essays, or e-rater
* E-rater gave significantly lower score than human rater when assessing African-American students’ written responses to issue prompt in GRE

Jiang & Pardos (2021) [https://dl.acm.org/doi/pdf/10.1145/3461702.3462623 pdf]
* Predicting university course grades using LSTM
* Roughly equal accuracy across racial groups
* Slightly better accuracy (~1%) across racial groups when including race in model

Zhang et al. (in press)
* Detecting student use of self-regulated learning(SRL) in mathematical problem-solving process
* For each SRL-related detector, relatively small differences in AUC were observed across racial/ethnic groups.
* No racial/ethnic group consistently had best-performing detectors

Latino/Latina/Latinx/Hispanic Learners in North America

2022-06-03T17:22:02Z

Joyce:

Anderson et al. (2019) [https://www.upenn.edu/learninganalytics/ryanbaker/EDM2019_paper56.pdf pdf]
* Models predicting six-year college graduation
* False negatives rates were greater for Latino students when Decision Tree and Random Forest yielded was used
* White students had higher false positive rates across all models, Decision Tree, SVM, Logistic Regression, Random Forest, and SGD

Christie et al. (2019) [https://files.eric.ed.gov/fulltext/ED599217.pdf pdf]
* Models predicting student's high school dropout
* The decision trees showed little difference in AUC among White, Black, Hispanic, Asian, American Indian and Alaska Native, and Native Hawaiian and Pacific Islander.

Lee and Kizilcec (2020) [https://arxiv.org/pdf/2007.00088.pdf pdf]
* Models predicting college success (or median grade or above)
* Random forest algorithms performed significantly worse for underrepresented minority students (URM; American Indian, Black, Hawaiian or Pacific Islander, Hispanic, and Multicultural) than non-URM students (White and Asian)
* The fairness of the model, namely demographic parity and equality of opportunity, as well as its accuracy, improved after correcting the threshold values from 0.5 to group-specific values

Yu et al. (2020) [https://files.eric.ed.gov/fulltext/ED608066.pdf pdf]
* Model predicting undergraduate short-term (course grades) and long-term (average GPA) success
* Hispanic students were inaccurately predicted to perform worse for both short-term and long-term
* The fairness of models improved when either click or a combination of click and survey data, and not institutional data, was included in the model

Yu et al. (2021) [https://dl.acm.org/doi/pdf/10.1145/3430895.3460139 pdf]
* Models predicting college dropout for students in residential and fully online program
* Whether the socio-demographic information was included or not, the model showed worse true negative rates for students who are underrepresented minority (URM; or not White or Asian), and worse accuracy if URM students are studying in person
* The model showed better recall for URM students, whether they were in residential or online program

Bridgeman et al. (2009) [https://www.researchgate.net/publication/242203403_Considering_Fairness_and_Validity_in_Evaluating_Automated_Scoring page]
* Automated scoring models for evaluating English essays, or e-rater
* E-Rater gave significantly better scores than human rater for 11th grade essays written by Hispanic students and Asian-American students

Jiang & Pardos (2021) [https://dl.acm.org/doi/pdf/10.1145/3461702.3462623 pdf]
* Predicting university course grades using LSTM
* Roughly equal accuracy across racial groups
* Slightly better accuracy (~1%) across racial groups when including race in model

Zhang et al. (in press)
* Detecting student use of self-regulated learning (SRL) in mathematical problem-solving process
* For each SRL-related detector, relatively small differences in AUC were observed across racial/ethnic groups.
* No racial/ethnic group consistently had best-performing detectors

Black/African-American Learners in North America

2022-06-03T17:19:56Z

Joyce:

Kai et al. (2017) [https://www.upenn.edu/learninganalytics/ryanbaker/DLRN-eVersity.pdf pdf]
* Models predicting student retention in an online college program
* J48 decision trees achieved much lower Kappa and AUC for Black students than White students
* JRip decision rules achieved almost identical Kappa and AUC for Black students and White students

Hu and Rangwala (2020) [https://files.eric.ed.gov/fulltext/ED608050.pdf pdf]
* Models predicting if a college student will fail in a course
* Multiple cooperative classifier model (MCCM) model was the best at reducing bias, or discrimination against African-American students, while other models (particularly Logistic Regression and Rawlsian Fairness) performed far worse
* The level of bias was inconsistent across courses, with MCCM prediction showing the least bias for Psychology and the greatest bias for Computer Science

Christie et al. (2019) [https://files.eric.ed.gov/fulltext/ED599217.pdf pdf]
* Models predicting student's high school dropout
* The decision trees showed little difference in AUC among White, Black, Hispanic, Asian, American Indian and Alaska Native, and Native Hawaiian and Pacific Islander.

Lee and Kizilcec (2020) [https://arxiv.org/pdf/2007.00088.pdf pdf]
* Models predicting college success (or median grade or above)
* Random forest algorithms performed significantly worse for underrepresented minority students (URM; American Indian, Black, Hawaiian or Pacific Islander, Hispanic, and Multicultural) than non-URM students (White and Asian)
* The fairness of the model, namely demographic parity and equality of opportunity, as well as its accuracy, improved after correcting the threshold values from 0.5 to group-specific values

Yu et al. (2020) [https://files.eric.ed.gov/fulltext/ED608066.pdf pdf]
* Model predicting undergraduate short-term (course grades) and long-term (average GPA) success
* Black students were inaccurately predicted to perform worse for both short-term and long-term
* The fairness of models improved when either click or a combination of click and survey data, and not institutional data, was included in the model

Yu et al. (2021) [https://dl.acm.org/doi/pdf/10.1145/3430895.3460139 pdf]
* Models predicting college dropout for students in residential and fully online program
* Whether the socio-demographic information was included or not, the model showed worse true negative rates for students who are underrepresented minority (URM; or not White or Asian), and worse accuracy if URM students are studying in person
* The model showed better recall for URM students, whether they were in residential or online program

Ramineni & Williamson (2018) [https://files.eric.ed.gov/fulltext/EJ1202928.pdf pdf]
* Revised automated scoring engine for assessing GRE essay
* E-rater gave African American test-takers significantly lower scores than human raters when assessing their written responses to argument prompts
* The shorter essays written by African American test-takers were more likely to receive lower scores as showing weakness in content and organization

Bridgeman et al. (2009) [https://www.researchgate.net/publication/242203403_Considering_Fairness_and_Validity_in_Evaluating_Automated_Scoring pdf]
* Automated scoring models for evaluating English essays, or e-rater
* The score difference between human rater and e-rater was significantly smaller for 11th grade essays written by White and African American students

Bridgeman et al. (2012) [https://www.tandfonline.com/doi/pdf/10.1080/08957347.2012.635502 pdf]
* A later version of automated scoring models for evaluating English essays, or e-rater
* E-rater gave significantly lower score than human rater when assessing African-American students’ written responses to issue prompt in GRE

Jiang & Pardos (2021) [https://dl.acm.org/doi/pdf/10.1145/3461702.3462623 pdf]
* Predicting university course grades using LSTM
* Roughly equal accuracy across racial groups
* Slightly better accuracy (~1%) across racial groups when including race in model

Zhang et al. (in press)
* Detecting student use of self-regulated learning strategies (SRL) in mathematical problem-solving process
* For each SRL-related detector, relatively small differences in AUC were observed across racial/ethnic groups.
* No racial/ethnic group consistently had best-performing detectors

Black/African-American Learners in North America

2022-06-03T17:19:27Z

Joyce:

Kai et al. (2017) [https://www.upenn.edu/learninganalytics/ryanbaker/DLRN-eVersity.pdf pdf]
* Models predicting student retention in an online college program
* J48 decision trees achieved much lower Kappa and AUC for Black students than White students
* JRip decision rules achieved almost identical Kappa and AUC for Black students and White students

Hu and Rangwala (2020) [https://files.eric.ed.gov/fulltext/ED608050.pdf pdf]
* Models predicting if a college student will fail in a course
* Multiple cooperative classifier model (MCCM) model was the best at reducing bias, or discrimination against African-American students, while other models (particularly Logistic Regression and Rawlsian Fairness) performed far worse
* The level of bias was inconsistent across courses, with MCCM prediction showing the least bias for Psychology and the greatest bias for Computer Science

Christie et al. (2019) [https://files.eric.ed.gov/fulltext/ED599217.pdf pdf]
* Models predicting student's high school dropout
* The decision trees showed little difference in AUC among White, Black, Hispanic, Asian, American Indian and Alaska Native, and Native Hawaiian and Pacific Islander.

Lee and Kizilcec (2020) [https://arxiv.org/pdf/2007.00088.pdf pdf]
* Models predicting college success (or median grade or above)
* Random forest algorithms performed significantly worse for underrepresented minority students (URM; American Indian, Black, Hawaiian or Pacific Islander, Hispanic, and Multicultural) than non-URM students (White and Asian)
* The fairness of the model, namely demographic parity and equality of opportunity, as well as its accuracy, improved after correcting the threshold values from 0.5 to group-specific values

Yu et al. (2020) [https://files.eric.ed.gov/fulltext/ED608066.pdf pdf]
* Model predicting undergraduate short-term (course grades) and long-term (average GPA) success
* Black students were inaccurately predicted to perform worse for both short-term and long-term
* The fairness of models improved when either click or a combination of click and survey data, and not institutional data, was included in the model

Yu et al. (2021) [https://dl.acm.org/doi/pdf/10.1145/3430895.3460139 pdf]
* Models predicting college dropout for students in residential and fully online program
* Whether the socio-demographic information was included or not, the model showed worse true negative rates for students who are underrepresented minority (URM; or not White or Asian), and worse accuracy if URM students are studying in person
* The model showed better recall for URM students, whether they were in residential or online program

Ramineni & Williamson (2018) [https://files.eric.ed.gov/fulltext/EJ1202928.pdf pdf]
* Revised automated scoring engine for assessing GRE essay
* E-rater gave African American test-takers significantly lower scores than human raters when assessing their written responses to argument prompts
* The shorter essays written by African American test-takers were more likely to receive lower scores as showing weakness in content and organization

Bridgeman et al. (2009) [https://www.researchgate.net/publication/242203403_Considering_Fairness_and_Validity_in_Evaluating_Automated_Scoring pdf]
* Automated scoring models for evaluating English essays, or e-rater
* The score difference between human rater and e-rater was significantly smaller for 11th grade essays written by White and African American students

Bridgeman et al. (2012) [https://www.tandfonline.com/doi/pdf/10.1080/08957347.2012.635502 pdf]
* A later version of automated scoring models for evaluating English essays, or e-rater
* E-rater gave significantly lower score than human rater when assessing African-American students’ written responses to issue prompt in GRE

Jiang & Pardos (2021) [https://dl.acm.org/doi/pdf/10.1145/3461702.3462623 pdf]
* Predicting university course grades using LSTM
* Roughly equal accuracy across racial groups
* Slightly better accuracy (~1%) across racial groups when including race in model

Zhang et al. (in press)
* Detecting student use of self-regulated learning strategies (SRL) in mathematical problem-solving process
* For each SRL-related detector, relatively small differences in AUC were observed across racial/ethnic groups.
* No racial/ethnic group consistently had best-performing detectors

Self-regulated Learning

2022-06-01T16:44:05Z

Joyce:

Zhang et al. (in press) [pdf]
* Four detectors (i.e., numerical representation, contextual representation, outcome orientation, and data transformation) relating to two cognitive operations (assembling and translating) were built to detect middle school students' use of self-regulated learning in mathematical problem-solving process.
* Detectors were built using XGBoost with labels coded from text replays and features distilled from log data and textual responses.
* In each detector, relatively small differences in AUC were observed across gender and racial/ethnic groups, and no student group (either gender or racial/ethnic group) consistently had the best-performing detectors

Algorithmic Bias in Education

2022-06-01T15:57:54Z

Joyce: /* By Algorithm Application */

== Algorithmic Bias in Education ==

This Wiki summarizes the current evidence surrounding Algorithmic Bias in Education:
which groups are impacted, and in which contexts.

For a relatively recent review on this topic, see
Baker, R.S., Hawn, M.A. (in press) Algorithmic Bias in Education. To appear in <em>International Journal of Artificial Intelligence and Education</em>
([https://www.upenn.edu/learninganalytics/ryanbaker/AlgorithmicBiasInEducation_rsb3.7.pdf pdf])

== By Group Impacted ==
* Race and Ethnicity
** [[Black/African-American Learners in North America]]
** [[Latino/Latina/Latinx/Hispanic Learners in North America]]
** [[Asian/Asian-American Learners in North America]]
** [[White Learners in North America]]
** [[Indigenous Learners in North America]]
** [[Research on Race and Ethnicity Conducted Outside of North America]]
* [[Gender: Male/Female]]
* [[Gender: Non-Binary and Transgender Learners]]
* [[Sexual Orientation]]
* [[Linguistic Origin]]
* [[National Origin or National Location]]
* [[International Students]]
* [[Native Language and Dialect]]
* [[Learners with Disabilities]]
* [[Age]]
* [[Urbanicity]]
* [[Parental Educational Background]]
* [[Socioeconomic Status]]
* [[Military-Connected Status]]
* [[Children of Migrant Workers]]
* [[Religion and Religious Background]]
* [[Public or Private K-12 School]]
* [[Intersectional Research]]

== By Algorithm Application ==
* [[At-risk/Dropout/Stopout/Graduation Prediction]]
* [[Course Grade and GPA Prediction]]
*[[National and International Examination]]
* [[Short-term Performance and Learning Gains Prediction]]
* [[Automated Essay Scoring]]
* [[Speech Recognition for Education]]
* [[Other NLP Applications of Algorithms in Education]]
* [[Student Knowledge Modeling]]
* [[Engagement and Affect Detection]]
*[[Self-regulated Learning]]

Self-regulated Learning

2022-06-01T15:57:42Z

Joyce: Created page with "Zhang et al. (in press) [pdf] * Four detectors (i.e., numerical representation, contextual representation, outcome orientation, and data transformation) relating to two cognitive operations (assembling and translating) were built to detect middle school students' use of self-regulated learning in mathematical problem-solving process. * Detectors were built using XGBoost with labels coded from text replays and features distilled from log data and textual responses. * Com..."

Zhang et al. (in press) [pdf]
* Four detectors (i.e., numerical representation, contextual representation, outcome orientation, and data transformation) relating to two cognitive operations (assembling and translating) were built to detect middle school students' use of self-regulated learning in mathematical problem-solving process.
* Detectors were built using XGBoost with labels coded from text replays and features distilled from log data and textual responses.
* Comparing the AUC across student groups, relatively small differences were observed, and no student group (either gender or racial/ethnic group) consistently had the best-performing detectors

Algorithmic Bias in Education

2022-06-01T15:52:28Z

Joyce: /* By Algorithm Application */