A Free and Open Dataset from a Prototypical Data-driven Study
Assistant in Higher Education
Johannes Schrumpf
1,* a
, Felix Weber
2,* b
, Katharina Schurz
2,* c
, Niklas Dettmer
1d
and
Tobias Thelen
1e
1
Institute of Cognitive Science, Osnabrück University, Wachsbleiche 27, Osnabrück, Germany
2
virtUOS, Osnabrück University, Heger-Tor-Wall 12, Osnabrück, Germany
tthelen@uni-osnabrueck.de
Keywords: Artificial Intelligence, Dataset, Digital Study Assistant, Higher Education, Educational Recommendation
Engines.
Abstract: Digital study assistants (DSAs) are an as of yet sparsely explored method to build bridges between classical,
on-campus higher education and novel digital education opportunities. The DSA we present in this paper
(SIDDATA) aims at supporting students to identify, reflect upon and follow their personal educational goals.
Over the course of 11 months, students interacted with a prototype version 2.0 of the software, generating
data about what features were interacted with, users' study-related data, and which features were deemed as
useful. In this data paper, we present a preprocessed version of the DSA database for research in the domain
of digital higher education. We present the data model design of the DSA and its relation to its’ features. We
further expand on the data extraction method used to generate the present dataset from the DSA’s database.
We discuss potential research paths that can be explored based on the dataset as well as its limitations
1 INTRODUCTION
Digital higher education remains an innovative and
fast changing field: An ever-increasing assortment of
digital education opportunities blends, but also
partially clashes, with classical education formats
such as lectures and seminars (
Castro, 2019)
. At the
same time, emerging technologies such as Artificial
Intelligence (AI) find their way onto campuses not
only as a subject but also as a tool to support and
enhance student education.
Project SIDDATA (Studienindividualisierung
durch Digitale, Datengestützte Assistenten, eng:
study individualization through digital, data-driven,
assistants) aims to aggregate these education
opportunities and streamline them into an easy-to-use
DSA for long term usage, oriented towards individual
educational goals. As a collaboration project between
a
https://orcid.org/0000-0002-0068-273X
b
https://orcid.org/0000-0002-7012-3378
c
https://orcid.org/0000-0003-3804-1134
d
https://orcid.org/0000-0001-9944-1671
e
https://orcid.org/0000-0002-3337-6093
* Authors contributed equally
the University of Osnabrück, the University of
Bremen, and the Leibniz University of Hannover, it
is part of the “Innovationspotentiale Digitaler
Hochschulbildung” (eng: Innovation Potentials in
Digital Higher Education) funding line, funded by
the BMBF (Bundesministerium für Bildung und
Forschung, eng: Federal Ministry of Education and
Research).
The development of the SIDDATA digital study
assistant software (referenced as SIDDATA) has
occurred iteratively with an agile development
approach allowing user feedback to be incorporated
into the latter two of the three annual releases.
Research about goals in higher education and
requirements for DSAs can be found in the related
project publications (Reinken & Greiff, 2021,
Schrumpf et al., 2021, Thelen et al., 2019; Vogelsang
et al., 2019; Weber & Le Foll, 2020; Weber et al.,
Schrumpf, J., Weber, F., Schurz, K., Dettmer, N. and Thelen, T.
A Free and Open Dataset from a Prototypical Data-driven Study Assistant in Higher Education.
DOI: 10.5220/0011038800003182
In Proceedings of the 14th International Conference on Computer Supported Education (CSEDU 2022) - Volume 2, pages 155-162
ISBN: 978-989-758-562-3; ISSN: 2184-5026
Copyright
c
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
155
2019; Weber, 2019; Weber et al., 2021a, 2021b,
2021c; Weber & Thelen, 2021).
By collecting and processing educational resource
data on the one hand and user-system interaction, on
the other hand, the data collected by the DSA, version
P2, throughout its eleven months of operation offers
a unique opportunity to probe and analyze potential
opportunities and challenges for digital higher
education.
Thus, we here present data generated through the
interaction between users and SIDDATA. This
dataset (Weber et al., 2022) serves as a basis for free
exploration, offering insights into student interests,
users’ study-related data, the usefulness of features,
and their usage frequency. Subsets of this dataset
already have served as a basis for publications in the
domain of educational science (Schurz et al., 2021;
Weber et al., 2021c).
In the following, we give a brief overview of the
SIDDATA study assistant software architecture.
Further, we introduce SIDDATA’s data model and
briefly highlight the data extraction process. We then
list descriptive statistics characterizing the dataset.
We conclude with a discussion of potential research
avenues based on the present dataset as well as its
limitations.
2 BACKGROUND
The present data were collected from user interaction
with version 2.0 of the SIDDATA study assistant
software. Students from three German universities
used the software, and the dataset contains data from
all three universities. Data aggregation occurred over
the course of 11 months, from December 2020 to
November 2021.
Because the dataset follows the student assistant
software’s database structure, for a better
understanding, we present the software architecture
underlying the DSA. We highlight the hierarchical
structure of database objects on a conceptual level
and outline user-database object interactions
facilitated by the software.
2.1 Software Architecture
The SIDDATA study assistant software is
implemented as a web service by utilizing the django
web framework. Figure 1 illustrates the software
architecture and its components. The study assistant
software consists of two interlocking parts that
communicate via a RESTful-API: the SIDDATA
plugin (“frontend”) and the SIDDATA backend.
The frontend serves as a graphical user interface
integrated into an existing learning management
system (LMS) to incorporate DSA functionalities
without the need to integrate them into the core of the
LMS directly. SIDDATA was developed and tested
with the “Stud.IP” (see https://www.studip.de/) LMS.
Figure 1: The components of SIDDATA’s web-based
software architecture.
On the one hand, the frontend provides data such
as course or user information to the backend. On the
other hand, it serves as an interface between users and
SIDDATA backend to request and receive
functionalities implemented in the backend logic.
The SIDDATA backend implements and serves
SIDDATA functionalities. It receives and processes
data from the LMS via the SIDDATA plugin. It also
handles user interaction by evaluating user inputs and
by creating appropriate answers, as implemented in
individual backend functions. A central component of
the SIDDATA backend is the SIDDATA database.
This PostgreSQL database stores, manages, and
retrieves information on individual SIDDATA
functions, user data, educational resource data, and
the state of interaction between users and the study
assistant software.
2.2 Hierarchical Data Structure
SIDDATA’s data model follows a strict hierarchy:
The uppermost hierarchy level is formed by so-called
Recommender modules. Attached to these modules
are Goal type objects. Activity type objects are
attached to goals and form the building blocks for
user-system interactions. Recommenders can have
multiple Goals, and Goals can have multiple
Activities attached. Figure 2 illustrates the
hierarchical structure of the SIDDATA data model.
SIDDATA’s functionalities are realized in
Recommender modules. Each recommender aims at
supporting student self-regulated learning through
offering a specific service. Recommenders vary in
terms of complexity: While some implement, for
example, a simple guide for applying and pursuing a
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156
Figure 2: Hierarchical structure of the SIDDATA data
model.
semester abroad, others offer more complex
functionalities such as a natural language based
semantic search engine for educational resources
(Schrumpf et al., 2021).
Attached to each recommender are Goals. These
objects represent abstract, long-term, or persistent
sub-functionalities of Recommenders. One example
of a Goal is a guide for navigating the administrative
landscape for a semester abroad. Figure 3 shows an
example of a Goal as displayed for the user.
Figure 3: Example of a Goal type object from the
“Academic Interests” recommender. The student has
entered “climate change mitigation” as one of their
interests. The Goal object is displayed as the dark blue strip
(Climate Change mitigation) above an attached Activity
object (Mein Thema[German: My Topic]). Goal type
objects can hold multiple Activity type objects.
Goal type objects can be discarded by clicking the
trash can symbol on the top right of the frame. This
may be useful if users decide not to pursue a goal
further and want to remove it from the interface.
Attached to Goals are Activities; these represent
atomic actions the user can perform within the study
assistant software. An example of an Activity is
visiting the foreign office of one’s home university to
sign a learning agreement for a semester abroad.
Activity objects are commonly displayed as a
dedicated interactive graphic with a bounding box
(see Figure 4).
Figure 4: Example of an Activity type object as displayed
in a learning management system.
Activities can be discarded and snoozed by users.
A snoozed Activity is removed from the user
interface and listed under the “paused” tab. Such
Activities can be resumed at a later point. Discarded
Activities are logged under the “discarded” tab.
Finally, Activities that were completed successfully
are listed under the “completed” tab.
2.3 Recommender Modules
Recommenders provide specific functionalities of the
study assistant software. They serve as the highest
attachment point for Goal objects. From a technical
perspective, Recommenders have their own
interaction logic and handle the processing of
attached Goals and Activity objects. From the user
perspective, recommenders are sub-functions that can
be enabled and disabled and provide specific services,
such as finding personally relevant learning resources
(Fachliche Interessen, eng: Academic Interests) or
connecting to other learners (Get-together, eng:
Academic Contacts
).
Figure 5: Example of the SIDDATA navigation menu
displaying activated Recommenders above and links to
paused, discarded, and completed Recommenders below.
A Free and Open Dataset from a Prototypical Data-driven Study Assistant in Higher Education
157
Recommenders offer students the service to
engage with and reflect upon individual educational
goals associated with the aspect of studying
associated with a recommender’s coverage: The
“Open Educational Resource” recommender, for
example, introduces learners to and highlights key
properties of, OERs and how they can assist with
learning subjects relevant to the user. Recommenders
thus are to be interpreted as independent interactive
features of the study assistant system. Once enabled,
recommender modules appear in the navigation menu
and are accessible via the graphical user interface (see
Figure 5).
Our dataset holds information about which users
have activated individual Recommender modules, as
well as which Goals and Activities are attached to
Recommenders. Table 1 shows the Recommenders
for which data is available in our dataset.
Table 1: Recommenders and their function.
Recommender title
(German title)
Description
Academic Contacts
(Get-together)
A tool for students to connect,
based on their personal
matching preferences
Academic Interests
(Fachliche
Interessen)
Semantic search engine for
Educational Resources based
on natural language processing
(see Schrumpf et al., 2021)
Data Ethics
(Souverän mit
meinen Daten)
Providing information about
data ethical aspects of the
digital age
Evaluation
(Evaluation)
Survey for users to give
detailed feedback
Funding
(Studienfinanzierung)
Providing information on how
to find funding for one’s
studies
Learning
Organization
(Lernorganisation)
Providing information about
methods and practices for self-
regulated learning
Open Educational
Resources
(Freie
Bildungsmaterialien)
Providing information about
Open Educational Resources
and a list of preselected
repositories
Personality Module
(Persönlichkeitsmodul)
Personality test for measuring
task-switching ability and
short/long-term memory
Scientific Career
(Wissenschaftliche
Karriere)
Providing general information
about a career in the scientific
field
Study Abroad
(Auslandssemester)
Providing general information
about study abroad
SIDDATA’s design philosophy is based on a
constructivist learning ideal where intrinsically
motivated learners take responsibility for their
personal and intellectual development. At the base of
this philosophy stand personal, educational goals,
which is why the substructure of recommenders is
called “goals” (see goal-related references for more
information). For some of the goal entities in the data
set it is the case that they correspond to a specific
personal goal (such as a specific personal interest in a
topic or a semester abroad), while some goal entities
in the data set were created by the developers to
structure the program flow and the appearance to the
user.
For instance, if students enter a text describing a
topic of personal interest in the Academic Interests
recommender, a goal appears with the interest as title,
and course recommendations appear as activities
within this goal. The goal here stands for an interest a
student wants to follow while studying. In contrast, a
goal within the Semester Abroad recommender is, for
example, to find a partner university with a fitting
study program. Here, a goal stands for a milestone in
the process of studying a semester abroad.
Activities are the smallest units in the data
structure and can be understood as atomic user
interactions with the system. Examples for Activities
are recommendations generated and displayed to the
user or questions which ask the users for input. The
term Activity was chosen because the system is
intended to elicit actions of students towards their
individual goals. All activity type objects have a title
and a description displayed directly in the UI of the
SIDDATA frontend. Additionally, Activities can be
paused or discarded. If Activities are successfully
interacted with, they are moved to the finished tab and
automatically set to inactive. Activities are sorted into
different types, each one implementing a unique
mode of interaction:
To-do type Activities act as an item on a checklist,
displaying information or reminding the user of a task
to be fulfilled to progress in reaching a certain goal.
Question type Activities require the user to make a
choice from a pre-selected list of answers or query the
user for an answer in natural language.
Resource type Activities present online resources in
the form of a weblink. This data type is intended to
represent a broad range of educational resources, such
as Open Educational Resources (OER), Massive
Open Online Courses (MOOCs), or goal-relevant
online resources, such as for instance a web-page of
an institution offering scholarships that the digital
assistant may identify with being of interest for the
personal goal to study abroad.
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158
Course type Activities are a recommendation for
enrollment into a course from the LMS of the local
and partner universities.
Additionally, users can rate any Activity on a
scale of 0 to 4. This feedback function provides hints
for the quality of the recommended items.
2.4 Data Extraction & Processing
In the SIDDATA database, long and cryptic IDs are
used to identify activities, students, recommenders,
courses, degrees, and goals for performance and
security reasons. To increase usability for researchers
working with the dataset and to further anonymize the
data to make it even less person-relatable, these IDs
were incrementally replaced by plain integer values.
In the original database, some personal or
personal-relatable data is present, which had to be
removed from the dataset. This includes names,
telephone numbers, and email addresses exchanged
between students in the Academic Contacts
recommender. In some cases, other personal
information such as health-related information was
present. Information of these kinds was replaced by
placeholders, such as for instance <emailadress>.
This procedure substitutes personal data in a way that
allows researchers to understand and analyze the data
while effectively protecting person-relatable
information.
3 DATASET PROPERTIES
The core objective of data processing from the
original data, stored in a PostgreSQL relational
database to one single CSV file, was optimal usability
for reuse by the scientific community. While the
database model was designed to avoid redundancies,
the preprocessed data set repeats information and at
the same time allows filtering, aggregation, and
custom analyses with all kinds of data processing
tools capable of reading CSV files, such as
LibreOffice Calc (or a commercial alternative, such
as Excel) or data analysis tools, such as R or PSPP (or
a commercial alternative, such as SPSS). The size of
the resulting comma-separated-value file (.csv) is
20.1 MB. The file format allows the comma or
semicolon as a string separator. In this case, the
semicolon was chosen because included texts do
contain commata, which leads to formatting issues
when working with the data. For scientists working
with the data, it is important to choose a semicolon
and not the comma as a string separator when
importing or reading the csv file.
3.1 Dataset Structure and Statistics
To make the dataset more readable, this section
introduces the data structure.
Inheriting from the hierarchical data structure of
the SIDDATA database, each activity belongs to
exactly one Goal, which belongs to exactly one
Recommender. Additionally, we have associated
each activity with a corresponding student interacting
with this activity. Hence, we aggregate all activity,
goal, and student information in one row of the
dataset. This enhances readability with the tradeoff of
increased repetition.
Students possess additional attributes such as field
of study, semester of enrollment, and home
university. Students can also study in multiple study
programs and, therefore, different semesters of
enrollment for each of their study programs. We have
aggregated student information within squared
brackets for dataset columns with potentially multiple
entries meaning that each indexed item within the
squared brackets corresponds to the data at the same
index in another column with multiple entries. For
example, if a student studies in two fields of study,
[mathematics, philosophy], the corresponding
semester of enrollment is stored as [3,5], where 3
corresponds to “mathematics” and 5 corresponds to
“philosophy”, in the semester of enrollment column.
Filtering functions allow to “zoom” into the data
to inspect, for instance, all activities of a user, all
users of a university, all students enrolled into a
specific study program, etc. Table 2 gives a detailed
explanation of variables and a range of possible
values for each column of the dataset.
Table 2: Summary of columns present in the dataset with a
more in-depth explanation of column meaning.
Column Explanation
activity_id unique identifier for activities,
se
q
uential numberin
g
activity_title
title of the activity
activity_description
description text
activity_status status of interactions (possible
values: “new”, “active”,
”snoozed”, ”done”, “discarded”,
”active”
)
activity_type type of activity (possible values:
“todo”, ”question”, ”resource”,
”course”)
feedback_scale_size Number of choices the user has to
rate an Activity. Ranges from 2 to
5, 0 stands for no feedback
o
p
tion.
A Free and Open Dataset from a Prototypical Data-driven Study Assistant in Higher Education
159
Table 2: Summary of columns present in the dataset with a
more in-depth explanation of column meaning (cont.).
Column Explanation
Given_feedback Feedback value for an Activity if it
was rated b
y
the use
r
timestam
p
time of creation
question_id unique identifier for questions,
sequential numbering (optional,
only for questions)
question question text (optional, only for
q
uestions
)
answers set of possible answers for multiple
choice questions (optional)
given_answers answers given by the student
(
o
p
tional
)
course_id unique identifier for courses,
sequential numbering (optional,
only for courses)
resource_id unique identifier for resources,
sequential numbering (optional,
only for recommended resources)
resource_title title of resource (optional, only for
resources)
resource_url url of a resource (optional, only for
resources
course_title title of a recommended course
(
o
p
tional, onl
y
for courses
)
goal_id unique identifier for goal,
sequential numberings
goal_title short title of a goal
goal_description detailed description of a goal
(
o
p
tional
)
goal_order order in which the goal is displayed
in the recommende
r
recommender name of the recommender
student_id unique identifier for students,
sequential numbering
student_university home university of the student
student_institute institute of the student (optional)
subject* study subject of the student
(optional)
subject_id unique identifier for subjects,
se
q
uential numberin
g
degree_id unique identifier for degrees,
se
q
uential numberin
g
degree* target degree for a subject of the
student
semester* semester for each combination or
subject and degree for a specific
student
*Subject, degree and semester together state an enrollment
of a student into a study program. One student can be
enrolled in more than one study program. In this dataset,
subject, degree, and semester contain ordered lists. The
order clarifies which subject, degree, and semester together
constitute one enrollment. The number of known
enrollments per student range from 0 to 5.
Table 3 shows the numbers for each entity type
represented in the data.
Table 3: The dataset holds information about 735 students
from three universities and the following other entity types
and quantities.
Entity Type n
Universities 3
Stud
y
Sub
j
ects 165
De
g
rees 19
Students 735
Recommende
r
9
Goals 6081
Activities 41520
Courses 1365
Questions 899
Semesters 15
4 DISCUSSION
With the SIDDATA prototype 2 dataset, we present a
source of data for investigations in the domain of
study assistant engineering, digital education,
educational psychology, recommendation engine
evaluation and the strategic considerations for
learning in the digital age.
The present dataset already served as a basis to
perform an effectiveness analysis of the SIDDATA
assistant software (see Schurz et al., 2021). However,
with this first publication, the depths of the
SIDDATA prototype 2 dataset are far from being
used to exhaustion.
4.1 Potential Research Avenues
We have identified the following paths as of yet
unexplored research opportunities for future
scientific endeavors:
So far, we have not performed analysis into
student-system interaction based on the field of study.
Aggregating statistical information about such usage
could produce novel insights into the student behavior
in terms of technology openness, the effectiveness of
software features, or how well the software design fits
the needs of students from different domains of
expertise. General statistical information about the
number of enrolled students per field of study is
available at each partner university's webpage (see
Appendix) and, in conjunction with the present
dataset, reveals new information.
Further, we have not analyzed the feedback from
students on single Activity objects, aside from a
general trend which indicates which Activities from
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160
which Recommender was generally perceived to be
useful or less useful, interactions between the
semester of enrolment, the field of study, and
likelihood to rate certain Activities with certain
values may be discoverable.
Additionally, an analysis of when which
recommenders were used may also give insights into
the needs and interests of students over the course of
two semesters.
Regarding individual recommenders, there are
multiple open questions that, as of yet, have not been
examined in detail:
The Academic Interests recommender (Fachliche
Interessen) logs interests that were entered by
students. These interests have not been analyzed for
their content or correlation with the field of study,
semester of enrollment, or other student data. Here,
an analysis of feedback for given recommendations
could yield insights into how fitting
recommendations were to requests.
Another recommender whose investigation could
hold valuable insight is the Academic Contacts (Get-
together) recommender: Here, students are able to get
matched to other students based on their common
interests. Analyzing the common interests students
have entered in order to find matches could give a
perspective on what kind of extracurricular
workshops or events could be offered by student
bodies or by other university institutions to
effectively facilitate student interest networks.
The present dataset may also support studies that
evaluate digital study assistant systems in general or
that seek to assess the state of digital higher education
in general.
4.2 Limitations
The dataset presented here offers a variety of data for
exploring the effectiveness of a digital study assistant
system. However, some limitations have to be noted,
which may impact future findings from the present
data: Before using the study assistant software, users
were asked whether they were willing to share their
data for research purposes. The data in the dataset
presented here, therefore, is only composed of
interactions logged from users who affirmed the
usage of their data. Therefore, a confounding effect
between willingness to share data and the willingness
to explore and interact with the student assistant
software caused by general user technology
acceptance cannot be ruled out.
Another limitation of the dataset is the quality of
data: Even though general data quality can be
considered high, some data such as field of study or
the title of recommended courses are drawn from the
learning management system of participating
universities. This means that the data quality of these
items may vary, as learning management system
settings for one university (Bremen) allowed students
to edit their field of study, while the other two
universities (Hannover, Osnabrück) only gave
officials the writing permissions for these data fields.
For courses, some course titles may only give
information in context and for a specific field of
study. This holds, for example, for acronyms that
carry little meaning outside a field. Further, semesters
of enrollment and users enrolled in a specific field of
study are not evenly distributed. This means that
investigations relying on semesters of enrollment and
field of study may not yield representative results and
may need to be pre-selected for a sufficient number
of samples before they can be used in a study.
4.3 Outlook
Since November 2021, the third and final version of
the software prototype has been released and is
running productively at the three partner universities.
This new version includes more and refined features
such as the inclusion recommendations for MOOCs
as motivated by Vogelsang et al. (2019), an
interactive tool for hierarchical goal setting as per
Weber et al. (2021b). The feature set has been
extended and improved, but the general database
structure has been maintained and refined. In the first
half of 2022, a new dataset with a compatible
structure will be published, which allows the reuse of
data analyses scripts and methods developed for the
dataset published along with this paper on new data.
Because the features the SIDDATA study
assistant software is offering are realized in the
SIDDATA backend, data from new LMS systems are
easy to integrate into the existing software. This
allows to expand SIDDATA’s reach beyond the three
partner universities and, potentially, to extend the
digital portfolio of many universities with low cost
and low maintenance requirements.
ACKNOWLEDGEMENTS
The authors acknowledge the financial support by the
Federal Ministry of Education and Research of
Germany for Siddata (project number 16DHB2124).
We thank all students who donated their data.
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161
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KE, osnaData, V1
APPENDIX
Statistics on student enrollment can be found for each
involved university by following the provided links:
Leibniz University of Hannover:
https://www.uni-hannover.de/de/universitaet/profil/
zahlen/studierendenstatistik/
University of Bremen:
https://www.finanzcontrolling.uni-bremen.de/daten/
index.htm
University of Osnabrück:
https://www.uni-osnabrueck.de/universitaet/zahlen
datenfakten/studierendenstatistiken/
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