Fairness in Learning Analytics: Student At-risk Prediction in Virtual

Learning Environments

Shirin Riazy, Katharina Simbeck and Vanessa Schreck

Hochschule f

ur Technik und Wirtschaft, Berlin, Germany

Keywords:

Learning Analytics, At-risk Prediction, Moocs, Fairness.

Abstract:

While the current literature on algorithmic fairness has rapidly expanded over the past years, it has yet to fully

arrive in educational contexts, namely, learning analytics. In the present paper, we examine possible forms

of discrimination, as well as ways to measure and establish fairness in virtual learning environments. The

prediction of students’ course outcome is conducted on a VLE dataset and analyzed with respect to fairness.

Two measures are recommended for the prior investigation of learning data, to ensure their balance and ﬁtness

for further data analysis.

1 INTRODUCTION

Following the outcry after the release of an article crit-

icizing racial bias in the prediction of recidivism of

criminal offenders (Larson et al., 2016), the debate on

algorithmic fairness has increased steadily over the last

years. However, even though a large number of fair-

ness measures has been developed (Verma and Rubin,

2018), these measures often only provide idealized

notions of fairness (Dwork et al., 2012; Hardt et al.,

2016) instead of tailored instructions. Furthermore,

they have mostly been tested and developed on the

same datasets

, leading to very limited coverage of the

different areas of machine learning applications.

The ﬁeld of learning analytics may be deﬁned as

the ”measurement, collection, analysis and reporting

of data about learners and their contexts, for the pur-

poses of understanding and optimizing learning and

environments in which it occurs.” (Siemens, 2013).

According to (Ebner and Ebner, 2018) and (Grandl

et al., 2017), ﬁve main goals of learning analytics

should be mentioned:

• Predictions and Interventions:

predictions of

student performance are made to provide them

with adequate interventions.

• Recommendations:

on the basis of analytics, rec-

ommendations for interventions are formulated.

The most popular examples are the ProPublica (COM-

PAS) data, the Ricci dataset, the Adult Income dataset and

the German Credit dataset (Friedler et al., 2019).

• Personalization and Adaptation:

based on the

learning activity, the learners are able to adapt

learning environments.

• Reﬂexion and Iteration:

the learners should be

able to reﬂect on their learning process.

• Benchmarking:

student performance is analyzed

in order to evaluate methods or learning content.

In order to make predictions, recommendations or

adaptations to the learning environments, it is common

for either data mining or machine learning tools to be

used on student data (Ochoa and Merceron, 2018).

However, many machine learning algorithms are used

as black-boxes and have yet to be thoroughly examined

in terms of fairness. Furthermore, the fairness of an

algorithm depends on its context, as the decision to be

made determines what possible disadvantages might

arise (Chouldechova et al., 2018; O’Neill, 2016).

A large percentage of recent contributions to ma-

jor learning analytics conferences (Learning Analytics

and Knowledge, 2019; Ochoa and Merceron, 2018)

deals with the prediction of student performance,

speciﬁcally whether a student will pass a course. The

students predicted to fail, called at-risk students, can

subsequently be provided with interventions, recom-

mendations and possibilities of adapting their learning

environment.

The fairness and validity of these algorithms is

essential because predictions of student performance

have an impact on student success (Rosenthal and Ru-

bin, 1978) and may thus perpetuate or even increase

biases in the data (O’Neill, 2016). Furthermore, a

Riazy, S., Simbeck, K. and Schreck, V.

Fairness in Learning Analytics: Student At-risk Prediction in Virtual Learning Environments.

DOI: 10.5220/0009324100150025

In Proceedings of the 12th International Conference on Computer Supported Education (CSEDU 2020) - Volume 1, pages 15-25

ISBN: 978-989-758-417-6

student being predicted a low performance and being

transferred to a low-level learning environment might

be stripped of their chances to perform well. Similarly,

a student who is challenged by the learning environ-

ment might not be able to thrive without adaptations.

In the following, we are going to review measures

and tactics of fairness regarding their suitability for

learning analytics (LA) contexts. To do so, we aim

to answer (at least in part) the following leading ques-

tions:

What types of discrimination may be distinguished

and measured with respect to LA?

2. How can fairness be measured in LA?

3. How can fairness be established in LA?

First, we want to review the existing fairness litera-

ture on the basis of predictions of the course outcome

of students in virtual learning environments. Using this

knowledge, we will replicate different models for stu-

dent at-risk prediction on an openly available dataset

and evaluate our research questions in light of the ex-

periment.

2 BACKGROUND

In order to derive answers to our leading questions,

we will review the current literature on ways of de-

tecting fairness as well as approaches to establishing

algorithmic fairness.

2.1 Types of Discrimination

In many cases, European and US-American law for-

bids discriminatory action (Ellis and Watson, 2012).

The US Civil Rights Act of 1964 (Brown, 2014) pro-

hibits discrimination in areas such as voting, public ac-

comodations, facilities and education. Speciﬁcally, the

Equal Employment Opportunity Commission (Davila

and Bohara, 1994) has outlined guidelines for the de-

tection of discrimination. The 80% rule represents the

permittable differences in rates of hiring, promotion

or other employment decision, between members of

certain races, sexes or ethnic groups. This is called an

adverse or disparate impact and has been picked up in

algorithmic fairness literature (Zafar et al., 2015; Cal-

mon et al., 2017). A common deﬁnition of disparate

impact (Friedler et al., 2019) for binary classiﬁcation

problems given two groups is

DI =

P ( Positive classiﬁcation | Group 1)

P ( Positive classiﬁcation | Group 2)

, (1)

DI,DI

−1

≥ 0.8. (2)

It is to be noted that even though the jurisdiction

penalizes discrimination, the attempts of deﬁning or

detecting implicit forms of discrimination have yet to

be reﬁned.

In current research, three types of discrimination

are often distinguished (Barocas and Selbst, 2016;

Kamishima et al., 2012):

1. Direct Discrimination/Disparate Treatment:

intentionally discriminating actions on the basis

of protected group membership. In many cases

illegal in European countries and the US (Feldman

et al., 2015).

2. Disparate Impact/Indirect Discrimination:

in-

tentionally or unintentionally discriminating ac-

tions without knowledge of group membership.

Not illegal in most cases (Barocas and Selbst,

2016). A rigorous mathematical deﬁnition of

disparate impact as well as different examples

of it (redlining, negative legacy, self-fulﬁlling

prophecy) are listed in (Feldman et al., 2015).

3. Underestimation:

Kamishima and colleagues de-

scribe a third source of discrimination, which

speciﬁcally occurs in algorithmic settings. Here,

the sample size of a learning algorithm is too small

(or unequally distributed), leading to discrimina-

tory suggestions, even if the setup of the algorithm

is otherwise fair (Kamishima et al., 2012).

All three types of discrimination could possibly

occur in a learning context. In learning analytics, di-

rect discrimination is very unlikely to appear, as this

would mean for makers of algorithms or interventions

to include rule-based decisions that disadvantage cer-

tain groups. Also, this type of discrimination would

be easily traceable and forbidden in many countries

(Ellis and Watson, 2012).

The second and third type of discrimination are

much more subtle. Machine learning algorithms have

been shown to produce outcomes with questionable

fairness (Adler et al., 2018; O’Neill, 2016). In learning

analytics, these very algorithms are used for predic-

tions, often of student performance. A simple form of

performance prediction is the binary classiﬁcation of

a student course outcome (Strecht et al., 2015; Yadav

et al., 2012).

2.2 Measurements of Fairness

The debate on algorithmic fairness has proposed a

wide range of fairness measures, applicable to different

scenarios and settings (Verma and Rubin, 2018). In

order to ﬁnd out which ways of measuring fairness

are applicable to learning analytics, we will review the

CSEDU 2020 - 12th International Conference on Computer Supported Education

current literature while splitting measures of fairness

into three categories:

1. Measurement of Balance in the Training Sets:

Calmon and colleagues (Calmon et al., 2017)

provide a probabilistic framework for the de-

tection (and removal) of bias in data. More-

over, information-theoretic measurements have

been introduced in order to measure the connec-

tion between group-afﬁliation and target labels

(Kamishima et al., 2012; Williamson and Menon,

2019).

2. Measurement of Fairness as an Algorithmic

Trait:

In order to understand and explain the traits

of algorithms – speciﬁcally deep learning methods

– explanations have focused on the processing and

representation of the data (Gilpin et al., 2018).

3. Outcome-based Fairness Mea-

surement:

Several authors

propose measures to certify fairness after

classiﬁcation (Kusner et al., 2017). These

measures usually rely on a confusion matrix (see

Section 2.2.3) and are especially in line with the

Equalized Odds deﬁnition of fairness (Hardt et al.,

2016).

Since, in learning analytics, any of these settings

might be relevant, we have picked out measures of fair-

ness from each of these categories in order to compare

them. In order to set the stage for these measures of

fairness, we will introduce our basic deﬁnitions.

Given a dataset

) ∈ X ×Y

with

i = 1,...,D

where the

∈ X

represent features of instances (such

as student data in a virtual learning environment) and

∈ Y

target labels (such a ﬁnal score in a course), pre-

dictions are made to infer labels of previously unseen

instances. Often we may assume

X = R

and

Y ⊆ R

where

is either continuous (regression problems)

or discrete (classiﬁcation problems). Predictions may

be written as parametrized maps

: X → Y

, which

may be qualitatively investigated using loss functions

l(Θ) = l (Θ, [x

]

i=1,...,D

)

, where

l(Θ)

measures how

well

ﬁts the data. Usually, one tries to ﬁnd a predic-

tion function that minimizes the loss of a prediction.

Furthermore, there have recently been suggestions

to add measures of fairness which additionally quantify

the fairness of a prediction with respect to a (protected)

group

. Most of the published fairness measures

focused on binary classiﬁcation (see Section 2.2.3).

In order to extend these measures, we will speciﬁ-

cally include fairness measures that do not focus on

discrete classiﬁcations but instead allow continuous

labels, such as a prediction of student scores.

2.2.1 Normalized Mutual Information

The normalized mutual information as a criterion for

fairness was introduced by (Kamishima et al., 2012).

Similar measures were also introduced by (Calmon

et al., 2017; Williamson and Menon, 2019). This

measure may be used to detect whether the training set

is balanced between groups and is targeted to identify

indirect discrimination. Since the deﬁnition of mutual

information is very general, it is versatile (may be

used for discrete as well as continuous data features

and/or labels) and applicable in different variants (may

be used during, before or after the application of an

algorithm).

In the discrete case, the normalized mutual infor-

mation consists of the mutual information

MI(Y ; G) =

∑

(y,g)∈D

Y,G

(y, g)log

Y,G

(y, g)

(y)P

(g)

(3)

and a normalization term, leading to

NMI(Y ; G) = MI(Y; G)/

H(Y )H(G) (4)

as the normalized mutual information, where

H(·)

an entropy function.

In practice, the probabilities

in (3) are replaced

by estimates

, e.g., the corresponding relative fre-

quencies in the training data or the outcomes

(Y )

the test data.

Note that the mutual information

MI(U;V )

be-

tween two random variables

and

measures the

dependency of these variables. Following the Group

Fairness deﬁnition (Dwork et al., 2012), the label

is hoped to be independent of the protected group

Furthermore, the denominator in (4) serves as a nor-

malization, which ensures

NMI ∈ [0,1]

, where

cor-

responds to independent variables, while

indicates

highest possible dependence. Mutual information may

be replaced by

-divergence (Komiyama and Shimao,

2017) or the Hilbert-Schmidt criterion (P

erez-Suay

et al., 2017) as different measures of distance in (4).

2.2.2 Underestimation Index

The underestimation index calculated the Hellinger

distance between two probability distributions, namely

the distributions of the outcomes of different groups

(Kamishima et al., 2012). This measure is targeted at

the third type of discrimination mentioned in Section

2.1, underestimation, and detects it as a property of the

algorithm by measuring its uncertainty.

Let

Y,G

be an estimator for

Y,G

, given by an

algorithm, and let

Y,G

be the distribution of the label

and the protected group in the training set. Then the

underestimation index is given by

Fairness in Learning Analytics: Student At-risk Prediction in Virtual Learning Environments

UEI(Y ;G) =

1 −

∑

(y,g)∈D

Y,G

(y, g)

Y,G

(y, g),

(5)

which is a reformulation of the Hellinger distance be-

tween

Y,G

(y, g) and

Y,G

(y, g).

2.2.3 Outcome-based Fairness

Many measures of fairness, mostly those built on the

Equalized Odds deﬁnition of fairness (Hardt et al.,

2016), work with elements of confusion matrices to

calculate differences between groups. These measures

fall in the third group of measures of fairness, the

outcome-based measures, and aim to detect indirect

discrimination. Since Equalized Odds requires the

group membership given the outcome to add no in-

formation to the prediction, various measures of fair-

ness for binary classiﬁcation were constructed, using

true/false positives/negatives (Verma and Rubin, 2018).

In the following, we will introduce Slicing Analysis,

which generalizes previous methods and has been used

in an educational context (Gardner et al., 2019).

Assume that we have a prediction

for binary

classiﬁcation and that the ﬁnal step of the algorithm

consists of thresholding. Let

(p)

be the random vari-

able of the p-score of a negative instance (which may

be below or above the threshold) and

(p)

denote the

random variable of the

-score of a positive instances,

then

AUC(p; G) = P (X

(p) > X

(p)|G) (6)

TPR

(p|G)



FPR

−1

(p|G)

(x)



dx (7)

denotes the area under curve, which may be used to

investigate algorithms, where

TPR

(p|G)

: R → [0, 1],

FPR

(p|G)

: R → [0, 1],

map classiﬁcation thresholds of

given a group

to their true/false positive rates. This leads us to the

fairness measure of Slicing Analysis, which is deﬁned

ABROCA(p) := AUC(p; g

) − AUC(p; g

). (8)

Since the AUC value is an accuracy value, we strive

for equal performance between groups, meaning that

the

ABROCA(p)

value should be as close to

as pos-

sible.

One can also deﬁne

ROC

(p|G)

(x) = TPR

(p|G)



FPR

−1

(p|G)

(x)



, (9)

which explains the name “ABROCA” as an abbre-

viation for the area between ROC curves. The area

between these curves should be as small as possible.

2.3 Establishment of Fairness

Several authors split the existing research on establish-

ing algorithmic fairness into three categories (Ruggieri

et al., 2010; Williamson and Menon, 2019; Calmon

et al., 2017; Zafar et al., 2015):

1. Fairness by Manipulation of Data (pre-

processing).

In (Zemel et al., 2013),

Zemel and colleagues propose an algorithm

which creates a representation of data where group

information is obfuscated. Calmon and colleagues

(Calmon et al., 2017) provide a probabilistic

framework for the detection and removal of bias

in data. Further nameworthy contributions were

made by auditing data (Adler et al., 2018) or by

reweighing/resampling data (Kamiran and Calders,

2012).

2. In-process Fairness by Optimization (con-

straints).

By using constraints, several researchers

have implemented algorithms which optimize fair-

ness as part of the learning algorithm. Zafar and

colleagues (Zafar et al., 2015) design a method

for convex margin-based classiﬁers (such as logis-

tic regression and SVM). In (Calders and Verwer,

2010), a naive Bayes approach is formulated, while

(Kamishima et al., 2012) deﬁnes an algorithm on

the basis of logistic regression and (Komiyama

et al., 2018) optimizes using a least squares regres-

sion. Recently, fairness optimizations using risk

measures have also been introduced (Williamson

and Menon, 2019).

3. Post-process Fairness.

Several authors propose

post-process corrections (Feldman et al., 2015;

Hardt et al., 2016) after using algorithmic classiﬁ-

cation. Calders and Verwer (Calders and Verwer,

2010) have offered a Bayesian approach for the

post-process correction of classiﬁed data.

In the following, we will examine how to establish

fairness in learning analytics, as the third of our leading

questions, and compare different algorithms, which

might be used in a learning analytics context. The pre-

and post-process correction methods ensure balances

(with respect to different metrics) in the training and

outcome data. In the case of pre-process alterations,

it is expected that these balances transfer to the clas-

siﬁed data. It seems, therefore, that these corrections

are mostly independent of the algorithms. Whether

fairness according to a certain measure (for example

the mean difference in the case of Calders (Calders

and Verwer, 2010)) implies fairness according to a dif-

ferent measure, however, would go beyond the scope

of this paper, as we try to restrict ourselves to possible

applications in the ﬁeld of learning analytics.

CSEDU 2020 - 12th International Conference on Computer Supported Education

Since the comparison of methods, not datasets, lies

in the focus of this paper, we focus on two in-process

fairness-optimization methods which we picked based

on the number of citations and their comparability. In

the following, we will brieﬂy introduce these algo-

rithms.

2.3.1 Kamishima’s Prejudice Remover

Kamishima and colleagues (Kamishima et al., 2012)

approach was to include a regularization term to di-

rectly decrease prejudice within a logistic regression.

To do so, they deﬁne a regularization term

(D;Θ) =

∑

)∈D

∑

y∈{0,1}

M[y|x

,Θ] log

(y|g

)

(y)

where

is a logistic regression prediction model. This

regularizaion term is closely related to the mutual in-

formation.

2.3.2 Zafar’s Margin-based Classiﬁer

The margin-based classiﬁer of Zafar and colleagues is

described in detail in (Zafar et al., 2015). Similar to

Kamishima’s Prejudice Remover (Kamishima et al.,

2012), they bounded their classiﬁer using a fairness

criterion, which was an extension of the DI measure

introduced in Section 2.1.

2.3.3 Trade-off between Fairness and

Performance

Note that many authors identify a trade-off between

fairness and performance of an algorithm (Menon and

Williamson, 2018; Komiyama et al., 2018; Friedler

et al., 2019). We would also like to verify this by us-

ing constraint-based algorithms and establishing their

accuracies.

2.4 Performance Prediction in Learning

Analytics

Understanding and improving students’ performance

plays an important role in producing work force and

innovators in the labor market (Yadav et al., 2012;

Chen et al., 2019; Shahiri et al., 2015). The university

of Porto, for example, has prioritized the modeling of

(un-)successful students in order to devise strategies

to reduce failures and understand general trends in

student performance (Strecht et al., 2015).

In order to accomodate different levels of academic

performances in an institution, data mining methods

are being used to mitigate failures and to better manage

resources (Migu

eis et al., 2018). Often, predictions

are made early on, in order to have more leeway for

interventions (Baneres et al., 2019).

Shahiri et al. (Shahiri et al., 2015) have conducted

a literature review on students’ performance prediction

using data mining techniques and have summarized

typical features used in such analyses. They have

found that

the cumulative grade point average (CGPA) was

most frequently used as the main attribute to pre-

dict student performance. According to the authors,

the reason for this might be that the CGPA has a

tangible value for future educational and career

mobility (Shahiri et al., 2015).

the second most often used attributes were demo-

graphic and external assessments, extra-curricular

activities, high school background and social in-

teractions (Shahiri et al., 2015; Oladokun et al.,

2008).

Several researchers use internal factors, such as

tendencies to procrastinate (Michinov et al., 2011),

persistence (Morris et al., 2005), engagement (Ander-

son et al., 2014), or other internal factors (Angeline,

2013) as a basis for the classiﬁcation. Chen et al.

(Chen et al., 2019) have conducted a feature analy-

sis of demographic, internal and external features and

have found that, among other things, gender and lo-

cation were strong predictors for poor performance

in online learning. Furthermore, Chen et al. (Chen

et al., 2019) divide the online behavior of students into

four categories: operational behaviors, cognitive be-

haviors, collaborative behaviors and problem-solving

behaviors (Peng, 2013).

2.5 Ethics in Learning Analytics

Ethical guidelines for educational research have ex-

isted for decades (Cohen et al., 2002) and include

the demands for informed consent, privacy, non-

maleﬁcence and human dignity among other things

(Cohen et al., 2002). In recent years, with the evolu-

tion of learning analytics as an autonomous research

area, several more taylored guidelines and frameworks

have been published (Yun et al., 2019; Welsh and

McKinney, 2015; Slade and Prinsloo, 2013; Drachsler

and Greller, 2016; Sclater and Bailey, 2015) to guide

researchers to use student data ethically.

Most of these guidelines focus on research prac-

tice, on consent and transparency or data ownership

(Prinsloo and Slade, 2017; Drachsler and Greller,

2016; Ferguson et al., 2016; Sclater and Bailey, 2015;

Sclater, 2016). Also, privacy and legal responsibilies

in experimental setups are relevant (Sclater and Bailey,

Fairness in Learning Analytics: Student At-risk Prediction in Virtual Learning Environments

2015; Ferguson et al., 2016; Drachsler and Greller,

2016).

Recently, researchers have argued that models

and algorithms should be “sound and free from bias”

(Slade and Boroowa, 2014; Sclater and Bailey, 2015).

However, they do not go into detail as to how data or

an algorithm can be balanced or even how to check for

balance.

3 DATASETS AND METHODS

In this section, we will introduce the data used as well

as the methods for the prediction of a course outcome.

3.1 OULAD Dataset

The OULAD dataset

is an open dataset published

by the Open University (Kuzilek et al., 2017). The

dataset contains anonymized student data from a vir-

tual learning environment (VLE) for seven courses in

the years 2013 and 2014. Furthermore, the OULAD

dataset contains data from roughly 30,000 students of

different gender, age and origin.

The ﬁrst group of variables entails the demographic

data of the students. This data contains typical demo-

graphic information which might be saved in a virtual

learning environment, such as the gender, age band

and highest education of a student. Also, whether or

not a student has declared a disability is recorded. In a

ﬁrst descriptive analysis, we see that the distribution of

the genders seems to be balanced, while the students

with declared disabilities vs. the ones without declared

disabilities are not balanced (see Figure 1).

The second group of features used were computed

from course-speciﬁc data and are explained in detail

in Table 1. First, a total of 25 features were generated

from the original data. In order to select the most rel-

evant features for the performance of the algorithm,

mutual information was used, yielding the most infor-

mative variables.

3.2 Algorithms for Course-outcome

Prediction

In the following, we will brieﬂy introduce the al-

gorithms used for the course-outcome prediction of

courses in the OULAD dataset. The training and test

data were split randomly for each execution of an al-

gorithm, where the training data was chosen to have

20%

of the samples and the training data to contain

The dataset is available under https://analyse.kmi.open.

ac.uk/open dataset.

no disability

disability

male

female

·10

number of students

Figure 1: Histogram of the Features for Declared Disability

and Gender.

Table 1: Variables and Virtual Learning Environment Data

Used as Features in the Classiﬁcation.

Variable Explanation

CMA count of computer-marked assign-

ment submitted

TMA count of tutor-marked assignment sub-

mitted

num_of_logins total number of logins

forumng number of clicks for this resource

page type

glossary number of clicks for this resource

page type

homepage number of clicks for this resource

page type

resource number of clicks for this resource

page type

the remaining samples. The ﬁrst group of algorithms

contains three types of logistic regression:

• Kamishima’s Prejudice Remover (KPR),

• Zafar’s Margin-Based Classiﬁer (ZMBC), and

• Classical Logistic Regression (LR),

where the second algorithm may be applied to any

margin-based classiﬁer. These algorithms, except for

the classical logistic regression, were introduced in

Section 2.3.

Further algorithms were picked by their usability

for the task at hand, the prediction of the course out-

come for students in a virtual learning environment:

• Naive Bayes (NB),

• Decision Tree Classiﬁer (DT), and

• Multi-Layer Perceptron (MLP).

CSEDU 2020 - 12th International Conference on Computer Supported Education

Table 2: Different Methods of at-Risk Prediction for the OULAD Dataset and Their Fairness, When Comparing Gen-

der/disability Groups. The Methods That Were Used without Sensitive Information Are Indicated with “ns” at the End. For

Easy Comparability, the DI and NMI Values in the Training and Test Sets Were Also Added.

Gender Disability

Methods Acc DI NMI UEI ABROCA DI NMI UEI ABROCA

Training 1.05 0.0008 0.84 0.0035

Test 1.06 0.0010 0.77 0.0077

KPR 96.8 1.07 0.0017 0.0093 0.0011 0.87 0.0031 0.1945 0.0155

ZMBC 96.7 1.08 0.0020 0.0078 0.0173 0.80 0.0081 0.1954 0.0080

LR 96.7 1.08 0.0019 0.0081 0.0196 0.80 0.0081 0.1967 0.0108

NB 90.9 0.97 0.0002 0.0056 0.0322 0.67 0.0190 0.1840 0.0169

DT 96.2 1.07 0.0010 0.0006 0.0017 0.74 0.0089 0.1446 0.0018

MLP 88.7 1.08 0.0013 0.0101 0.0081 0.84 0.0053 0.2057 0.0150

LRns 95.9 0.97 0.0003 0.0046 0.0101 0.80 0.0074 0.1883 0.0115

NBns 94.9 0.93 0.0014 0.0010 0.0165 0.80 0.0057 0.1589 0.0153

DTns 94.3 1.07 0.0009 0.0003 0.0109 0.86 0.0020 0.1230 0.0399

MLPns 85.7 0.99 0.0000 0.0073 0.0085 0.80 0.0078 0.2028 0.0203

Each of these algorithms has been used (at times

in different variations) in recent publications to predict

the performance of students in learning environments

udian et al., 2018; Yadav et al., 2012; Migu

eis et al.,

2018; Shahiri et al., 2015). Table 2 shows accuracy

and fairness measures for the prediction of course suc-

cess (passing) using those algorithms and comparing

fairness between genders and students with or without

declared disability.

In an attempt to test the best approach for the fair-

ness of algorithms, we will execute each of the afore-

mentioned algorithms in two variations: ﬁrst while

feeding them sensitive information (gender, declared

disability) as features and subsequently while leaving

them out. We will examine the impact this has on the

accuracy as well as the fairness of these algorithms.

The results in terms of accuracy and fairness of the

algorithms without sensitive features are displayed in

the lower part of Table 2.

4 RESULTS

In the present paper, we have developed and tested

several machine learning algorithms (listed in Sec-

tion 3.2) for the prediction of course outcomes in the

OULAD dataset. Among the algorithms, there were

also constraint-based algorithms which optimized fair-

ness.

The fairness measures used to evaluate these algo-

rithms were introduced in detail in Section 2.2. They

include:

1. Acc:

the accuracy of an algorithm, measured as

the rate of all correct classiﬁcations

2. DI: disparate impact

3. NMI: normalized mutual information

4. UEI: underestimation index

5. ABROCA:

differences between the area under

curve

Overall, the accuracies of all algorithms (Table 2)

were high enough to compare the student performance

prediction to similar publications (Strecht et al., 2015).

Table 2 compares the fairness of training and test data

and of the different models with regard to gender. A

DI value larger than one can be interpreted as a higher

probability for male participants to pass the course

in comparison to female participants. The disparate

impact of 5–6 percentage points in favor of male partic-

ipants from the training and test set is reproduced and

slightly ampliﬁed by most algorithmic approaches.

While the gender data is rather balanced, students

with declared disability are strongly underrepresented

in the data. However, the bias against students with de-

clared disability gets reproduced but not consistently

ampliﬁed. Again, the models tend to lose accuracy

when the sensitive attribute is not used, with the excep-

tion of the Naive Bayes classiﬁer.

When comparing the fairness measures for differ-

ent genders, most of the values of the different algo-

rithms are quite similar, except for the Naive Bayes

algorithm, which had the lowest NMI value and the

highest ABROCA value.

Moreover, it is notable that the KPR algorithm has

low NMI values, but otherwise, the constraint-based

algorithms (KPR and ZMBC) compare with the other

algorithms not only in accuracy but also in fairness.

Fairness in Learning Analytics: Student At-risk Prediction in Virtual Learning Environments

It is apparent that neither the accuracy of the classi-

ﬁcation nor their fairness differed much when leaving

out sensitive information.

0,002

0,004

0,006

0,008

0,01

0,012

0,014

0,016

0,018

0,02

0 0,2 0,4 0,6 0,8 1 1,2

NMI

Comparison of NMI and DI values

Figure 2: Comparison of NMI and DI Values for Different

Methods of Course-Outcome Prediction on the OULAD

Dataset.

0,05

0,1

0,15

0,2

0,25

0 0,005 0,01 0,015 0,02

UEI

NMI

Comparison of NMI and UEI values

Figure 3: Comparison of NMI and UEI Values for Different

Methods of Course-Outcome Prediction on the OULAD

Dataset.

We can see in Figures 2 and 3 that the NMI value

is negatively correlated to the DI value,

Corr(NMI, DI) = −0.8336, (10)

NMI, DI

= 7.8 · 10

−7

, (11)

and that the NMI value is positively correlated to the

UEI value,

Corr(NMI, UEI) = 0.7335, (12)

DI, UEI

= 3.5 · 10

−4

, (13)

where

NMI, DI

and

DI, UEI

are the

-values to the

corresponding

-tests. This supports the validity of the

NMI value as an indicator for the shared information

of a prediction outcome and group membership.

We can not support the feasibility of the ABROCA

measure as it was found by (Gardner et al., 2019).

This measure did not show signiﬁcant values for imbal-

anced group sizes nor imbalances in the group values.

As noted in Section 2.3.3, we would have expected

a trade-off between the performance (accuracy) of an

algorithm and its fairness. This was not conﬁrmed

when comparing the values of KPR, ZMBC and regu-

lar LR. We can see that, while KPR has the smallest

NMI values, the accuracy is the highest out of all three,

though only by a thousandth.

In a natural approach to make the algorithms more

independent of the sensitive attributes, gender and dis-

ability, we have also run the algorithms without feed-

ing them these attributes as features into the algorithms.

Overall, the fairness values slightly improved, though

disparate impact could still be detected for students

with declared disabilities (for LR and NB). Further-

more, the UEI values were similarly high for students

with and without declared disabilities. Similar results

have been found by (Kamishima et al., 2012; Zafar

et al., 2015).

5 DISCUSSION

Though fairness in education is highly relevant (Slade

and Boroowa, 2014; Sclater and Bailey, 2015), not

many publications deal with fairness in learning an-

alytics, speciﬁcally in virtual learning environments

(Gardner et al., 2019; Riazy and Simbeck, 2019). In

the following, we will discuss the results of the preced-

ing section, especially in order to explore the inﬂuence

of imbalances in training data to different algorithms

in learning analytics. In summary, our results are as

follows:

Depending on the balance of the training set, bias

may be reproduced or ampliﬁed.

UEI and NMI values reliably detected imbalances.

The constraint-based algorithms, as well as leaving

out sensitive information, slightly improved the

fairness values.

5.1 Reproduction and Ampliﬁcation of

Bias

For sensitive groups that are represented in a balanced

way in the dataset (in this case: gender), bias is re-

produced by the models. For sensitive groups that

CSEDU 2020 - 12th International Conference on Computer Supported Education

are underrepresented in the dataset (in this case: dis-

ability), bias is sometimes ampliﬁed and sometimes

decreased by the models. The effect of removing the

sensitive datapoints is erratic as well.

5.2 Measuring Bias

As one would expect, the underestimation index (UEI)

is generally rather low when comparing gender groups

and rather high when comparing the groups of students

who declared / did not declare a disability. Thus in our

case, the UEI was correctly able to identify imbalances

in the data. The NMI value correlated negatively with

the DI value and positively with the UEI value. This

supports its validity as a measure for informativity

of group membership. The ABROCA measure, an

initially promising fairness measure introduced in a

learning analytics context in (Gardner et al., 2019), did

not detect the disparate impact measured for students

with declared disability.

5.3 Mitigating Bias

As expected, a loss in accuracy is associated with leav-

ing out sensitive features. There was usually a loss of 1

to 3 percentage points in accuracy, except for the Naive

Bayes algorithm, where the accuracy increased by 4

percentage points. The constraint-based algorithms,

which optimized fairness, had slightly improved fair-

ness values when comparing declared disability.

6 CONCLUSION AND OUTLOOK

All in all, by following our three leading questions,

we have investigated possible ways of algorithmic dis-

crimination in learning analytics, and considered ways

to measure and mitigate them i.e., to establish fairness.

Since the prediction of student performance can

lead to interventions, recommendations or adaptation

of their learning environment, these decisions have to

be tested for their validity.

In the present paper, we have examined the

OULAD dataset, which contains real student data from

a virtual learning environment, and found a great un-

derrepresentation of students with declared disability.

This underrepresentation – in some cases – lead to

unfair classiﬁcations, meaning that students with de-

clared disabilities were predicted to fail courses with a

higher probability. This erratic behavior of the mod-

els, when a group is underrepresented, has yet to be

included in guidelines and ethical codes, to lead and

warn researchers when working with minorities. In

order to test for imbalances in the data, we suggest to

compute the UEI and NMI values. The DI measure,

with the 80%-threshold, presents an easy tool for the

determination of group differences. Its simplicity as

a group comparison makes it valuable as a marker in

order to ﬁnd (un-)fair classiﬁcations. For performance

prediction in learning analytics, we suggest a prior

analysis of the data using at least the three values: DI,

NMI and UEI.

In further research, we plan to investigate possibil-

ities for the comparison of continuous values, which

might be used in predictive tasks in learning analytics.

Here, we would like to include other accuracy-based

fairness measures, such as group comparisons of mean

squared error values. Furthermore, an in-depth analy-

sis is needed, in order to explain the different behavior

of the algorithms, especially those of the outliers, such

as the Naive Bayes algorithm.

ACKNOWLEDGEMENTS

This research has been funded by the Federal Ministry

of Education and Research of Germany in the frame-

work “Digitalisierung im Bildungsbereich” (project

number 01JD1812A).

REFERENCES

Adler, P., Falk, C., Friedler, S. A., Nix, T., Rybeck, G.,

Scheidegger, C., Smith, B., and Venkatasubramanian,

S. (2018). Auditing black-box models for indirect inﬂu-

ence. Knowledge and Information Systems, 54(1):95–

122.

Anderson, A., Huttenlocher, D., Kleinberg, J., and Leskovec,

J. (2014). Engaging with massive online courses. In

Proceedings of the 23rd international conference on

World wide web, pages 687–698. ACM.

Angeline, D. M. D. (2013). Association rule generation for

student performance analysis using apriori algorithm.

The SIJ Transactions on Computer Science Engineer-

ing & its Applications (CSEA), 1(1):12–16.

Baneres, D., Rodriguez-Gonzalez, M. E., and Serra, M.

(2019). An early feedback prediction system for learn-

ers at-risk within a ﬁrst-year higher education course.

IEEE Transactions on Learning Technologies.

Barocas, S. and Selbst, A. D. (2016). Big data’s disparate

impact. Calif. L. Rev., 104:671.

Brown, P. (2014). The civil rights act of 1964. Wash. UL

Rev., 92:527.

Calders, T. and Verwer, S. (2010). Three naive bayes ap-

proaches for discrimination-free classiﬁcation. Data

Mining and Knowledge Discovery, 21(2):277–292.

Calmon, F., Wei, D., Vinzamuri, B., Ramamurthy, K. N.,

and Varshney, K. R. (2017). Optimized pre-processing

Fairness in Learning Analytics: Student At-risk Prediction in Virtual Learning Environments

for discrimination prevention. In Advances in Neural

Information Processing Systems, pages 3992–4001.

Chen, Y., Zheng, Q., Ji, S., Tian, F., Zhu, H., and Liu, M.

(2019). Identifying at-risk students based on the phased

prediction model. Knowledge and Information Systems,

pages 1–17.

Chouldechova, A., Benavides-Prado, D., Fialko, O., and

Vaithianathan, R. (2018). A case study of algorithm-

assisted decision making in child maltreatment hotline

screening decisions. In Conference on Fairness, Ac-

countability and Transparency, pages 134–148.

Cohen, L., Manion, L., and Morrison, K. (2002). Research

methods in education. routledge.

Davila, A. and Bohara, A. K. (1994). Equal employment

opportunity across states: The eeoc 1979-1989. Public

Choice, 80(3/4):223–243.

Drachsler, H. and Greller, W. (2016). Privacy and analytics:

it’s a delicate issue a checklist for trusted learning

analytics. In Proceedings of the sixth international

conference on learning analytics & knowledge, pages

89–98. ACM.

Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel,

R. (2012). Fairness through awareness. In Proceedings

of the 3rd innovations in theoretical computer science

conference, pages 214–226. ACM.

Ebner, M. and Ebner, M. (2018). Learning analytics an

schulen–hintergrund und beispiele. Medienimpulse,

56(1).

Ellis, E. and Watson, P. (2012). EU anti-discrimination law.

OUP Oxford.

Feldman, M., Friedler, S. A., Moeller, J., Scheidegger, C.,

and Venkatasubramanian, S. (2015). Certifying and

removing disparate impact. In Proceedings of the 21th

ACM SIGKDD International Conference on Knowl-

edge Discovery and Data Mining, pages 259–268.

ACM.

Ferguson, R., Hoel, T., Scheffel, M., and Drachsler, H.

(2016). Guest editorial: Ethics and privacy in learning

analytics. SoLAR.

Friedler, S. A., Scheidegger, C., Venkatasubramanian, S.,

Choudhary, S., Hamilton, E. P., and Roth, D. (2019). A

comparative study of fairness-enhancing interventions

in machine learning. In Proceedings of the Conference

on Fairness, Accountability, and Transparency, pages

329–338. ACM.

Gardner, J., Brooks, C., and Baker, R. (2019). Evaluating the

fairness of predictive student models through slicing

analysis. In Proceedings of the 9th International Con-

ference on Learning Analytics & Knowledge, pages

225–234. ACM.

Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter,

M., and Kagal, L. (2018). Explaining explanations:

An overview of interpretability of machine learning.

In 2018 IEEE 5th International Conference on Data

Science and Advanced Analytics (DSAA), pages 80–89.

IEEE.

Grandl, M., Taraghi, B., Ebner, M., Leitner, P., and Ebner, M.

(2017). Learning analytics. Handbuch E-Learning: Ex-

pertenwissen aus Wissenschaft und Praxis-Strategien,

pages 1–16.

Hardt, M., Price, E., Srebro, N., et al. (2016). Equality of

opportunity in supervised learning. In Advances in

neural information processing systems, pages 3315–

3323.

Kamiran, F. and Calders, T. (2012). Data preprocessing

techniques for classiﬁcation without discrimination.

Knowledge and Information Systems, 33(1):1–33.

Kamishima, T., Akaho, S., Asoh, H., and Sakuma, J. (2012).

Fairness-aware classiﬁer with prejudice remover regu-

larizer. In Joint European Conference on Machine

Learning and Knowledge Discovery in Databases,

pages 35–50. Springer.

Komiyama, J. and Shimao, H. (2017). Two-stage algorithm

for fairness-aware machine learning. arXiv preprint

arXiv:1710.04924.

Komiyama, J., Takeda, A., Honda, J., and Shimao, H. (2018).

Nonconvex optimization for regression with fairness

constraints. In International Conference on Machine

Learning, pages 2742–2751.

Kusner, M. J., Loftus, J., Russell, C., and Silva, R. (2017).

Counterfactual fairness. In Advances in Neural Infor-

mation Processing Systems, pages 4066–4076.

Kuzilek, J., Hlosta, M., and Zdrahal, Z. (2017). Open

university learning analytics dataset. Scientiﬁc data,

4:170171.

Larson, J., Mattu, S., Kirchner, L., and Angwin, J. (2016).

How we analyzed the compas recidivism algorithm.

ProPublica (5 2016), 9.

Learning Analytics and Knowledge (2019). Proceedings of

the 9th international conference on learning analytics

& knowledge. New York, NY, USA. ACM.

Menon, A. K. and Williamson, R. C. (2018). The cost of

fairness in binary classiﬁcation. In Friedler, S. A. and

Wilson, C., editors, Proceedings of the 1st Conference

on Fairness, Accountability and Transparency, vol-

ume 81 of Proceedings of Machine Learning Research,

pages 107–118, New York, NY, USA. PMLR.

Michinov, N., Brunot, S., Le Bohec, O., Juhel, J., and

Delaval, M. (2011). Procrastination, participation, and

performance in online learning environments. Comput-

ers & Education, 56(1):243–252.

Migu

eis, V. L., Freitas, A., Garcia, P. J., and Silva, A. (2018).

Early segmentation of students according to their aca-

demic performance: A predictive modelling approach.

Decision Support Systems, 115:36–51.

Morris, L. V., Finnegan, C., and Wu, S.-S. (2005). Track-

ing student behavior, persistence, and achievement in

online courses. The Internet and Higher Education,

8(3):221–231.

Ochoa, X. and Merceron, A. (2018). Quantitative and qual-

itative analysis of the learning analytics and knowl-

edge conference 2018. Journal of Learning Analytics,

5(3):154–166.

Oladokun, V., Adebanjo, A., and Charles-Owaba, O. (2008).

Predicting students academic performance using arti-

ﬁcial neural network: A case study of an engineering

course.

O’Neill, C. (2016). Weapons of math destruction: How

big data increases inequality and threatens democracy.

Nueva York, NY: Crown Publishing Group.

CSEDU 2020 - 12th International Conference on Computer Supported Education

Peng, W. (2013). Analysis and modeling of e-learning be-

havior.

erez-Suay, A., Laparra, V., Mateo-Garc

ıa, G., Mu

noz-Mar

ı,

J., G

omez-Chova, L., and Camps-Valls, G. (2017).

Fair kernel learning. In Joint European Conference

on Machine Learning and Knowledge Discovery in

Databases, pages 339–355. Springer.

Prinsloo, P. and Slade, S. (2017). Ethics and learning analyt-

ics: Charting the (un) charted. SOLAR.

Riazy, S. and Simbeck, K. (2019). Predictive algorithms in

learning analytics and their fairness. In Pinkwart, N.

and Konert, J., editors, DELFI 2019, pages 223–228,

Bonn. Gesellschaft f

ur Informatik e.V.

Rosenthal, R. and Rubin, D. B. (1978). Interpersonal ex-

pectancy effects: The ﬁrst 345 studies. Behavioral and

Brain Sciences, 1(3):377–386.

udian, S., Lui, Z., and Pinkwart, N. (2018). Comparison

and prospect of two heaven approaches: Svm and ann

for identifying students’ learning performance. In 2018

Seventh International Conference of Educational In-

novation through Technology (EITT), pages 156–161.

IEEE.

Ruggieri, S., Pedreschi, D., and Turini, F. (2010). Data min-

ing for discrimination discovery. ACM Transactions

on Knowledge Discovery from Data (TKDD), 4(2):9.

Sclater, N. (2016). Developing a code of practice for learning

analytics. Journal of Learning Analytics, 3(1):16–42.

Sclater, N. and Bailey, P. (2015). Code of practice for learn-

ing analytics.

Shahiri, A. M., Husain, W., et al. (2015). A review on

predicting student’s performance using data mining

techniques. Procedia Computer Science, 72:414–422.

Siemens, G. (2013). Learning analytics: The emer-

gence of a discipline. American Behavioral Scientist,

57(10):1380–1400.

Slade, S. and Boroowa, A. (2014). Policy on ethical use

of student data for learning analytics. Milton Keynes:

Open University UK.

Slade, S. and Prinsloo, P. (2013). Learning analytics: Ethical

issues and dilemmas. American Behavioral Scientist,

57(10):1510–1529.

Strecht, P., Cruz, L., Soares, C., Mendes-Moreira, J., and

Abreu, R. (2015). A comparative study of classiﬁcation

and regression algorithms for modelling students’ aca-

demic performance. International Educational Data

Mining Society.

Verma, S. and Rubin, J. (2018). Fairness deﬁnitions ex-

plained. In 2018 IEEE/ACM International Workshop

on Software Fairness (FairWare), pages 1–7. IEEE.

Welsh, S. and McKinney, S. (2015). Clearing the fog: A

learning analytics code of practice.

Williamson, R. C. and Menon, A. K. (2019). Fairness risk

measures. arXiv preprint arXiv:1901.08665.

Yadav, S. K., Bharadwaj, B., and Pal, S. (2012). Data mining

applications: A comparative study for predicting stu-

dent’s performance. arXiv preprint arXiv:1202.4815.

Yun, H., Riazy, S., Fortenbacher, A., and Simbeck, K. (2019).

Code of practice for sensor-based learning. In Pinkwart,

N. and Konert, J., editors, DELFI 2019, pages 199–204,

Bonn. Gesellschaft fuer Informatik e.V.

Zafar, M. B., Valera, I., Rodriguez, M. G., and Gummadi,

K. P. (2015). Fairness constraints: Mechanisms for fair

classiﬁcation. arXiv preprint arXiv:1507.05259.

Zemel, R., Wu, Y., Swersky, K., Pitassi, T., and Dwork, C.

(2013). Learning fair representations. In International

Conference on Machine Learning, pages 325–333.

Fairness in Learning Analytics: Student At-risk Prediction in Virtual Learning Environments