Education is a complex domain where students must make their curricula choices carefully. To model the intricacies of the educational domain, ontologies have successfully been leveraged in the past. However, no available ontology or dataset directly addresses the critical transition from high school to university from a decision-making perspective. Therefore, the contribution of our on-going work is twofold. Firstly, we introduce EducOnto - an ontology that aims at modeling university curricula and students' profiles. Secondly, we introduce EduKG - a knowledge graph inheriting the semantics of EducOnto and instantiated with data about French students and curricula.
LGOBE (D. Monticolo) https://nicolas-hbt.github.io/ (N. Hubert) © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Hence, facing the need for public resources related to curriculum recommendation, we present an on-going work related to the development and provision of both a new ontology and a new dataset, referred to as EducOnto and EduKG, respectively. More specifically, EducOnto focuses on modeling the pivotal period bridging the last year of high school and the first years of university, while EduKG is a Knowledge Graph (KG) encompassing students’ profiles and academic training information. EduKG is built on the basis of EducOnto, from which it inherits its semantics and structural constraints.
In this section, we elaborate on the methodology used to design EducOnto. We follow best
practices guidelines presented in [
In the educational domain, several ontologies are publicly available1. However, none of them directly models the period ranging from the end of high school to the first years of university. EducOnto aims at modeling such a period. Together with education experts, we defined competency questions whose purpose is to frame the scope of the ontology and act as requirements that EducOnto should meet. The exhaustive list of competency questions is made available2. Let us introduce two of them as an example: CQ1. What are the recommended academic backgrounds for a specific university curriculum? CQ2. What is the most popular university curriculum following a given high school major?
With education experts, we first enumerate all the terms related to our application domain and the expected purpose of EducOnto. Two main concepts stand out: student and curriculum. Both concepts are related to other intermediate concepts such as major and specialty. Then, adopting a top-down development process, we end up with 71 classes and 30 object properties. A general overview of EducOnto that contains the main classes and properties is presented in Figure 1.
EducOnto can be considered as successfully designed if it helps to fulfill downstream tasks
related to university curricula information retrieval and recommendation. According to [
isInterestedIn
Personality
Trait
University Curriculum recommendsMajor curriculumRelatesTo
mentionedCurriculum belongsTo recommendsSpecialty keywordRelatesTo Keyword
Field of Study specialtyRelatesTo schoolSubjectRelatesTo
specialtyBelongsTo High School Major schoolSubjectBelongsTo
High School Specialty hasMainTopic (dis)likedMajor
(dis)likedSpecialty
School Subject hasFavoriteSubject hasPersonalityTrait
Student hasSkill
Academic Skill present the query for CQ1. More precisely, we retrieve the recommended high school majors to start a Bachelor’s degree in Computer Science (hereinafter referred to as curriculum:lg_cs).
Listing 1: SPARQL query to address CQ1 P R E F I X e d : < h t t p : / / p u r l . o r g / e d u c o n t o / > P R E F I X m a j o r : < h t t p : / / p u r l . o r g / e d u k g / m a j o r / > P R E F I X c u r r i c u l u m : < h t t p : / / p u r l . o r g / e d u k g / c u r r i c u l u m / > P R E F I X r d f : < h t t p : / / w w w . w 3 . o r g / 1 9 9 9 / 0 2 / 2 2 - r d f - s y n t a x - n s # > S E L E C T ? m a j o r W H E R E { ? m a j o r r d f : t y p e / r d f s : s u b C l a s s O f * e d : H i g h S c h o o l M a j o r .
c u r r i c u l u m : l g _ c s e d : r e c o m m e n d s H i g h S c h o o l M a j o r ? m a j o r . }
We will enrich EducOnto by linking it with other ontologies. As previously said, few ontologies model the period from the end of high school to the first years of university. However, some ontologies may be partially reused, e.g. EduProgression Ontology3. 3https://lov.linkeddata.es/dataset/lov/vocabs/edupro
In addition, we intend to develop collaborative work with other European institutions and education experts to broaden the scope of EducOnto to countries belonging to the European Higher Education Area4.
In this section, we detail the whole pipeline for the construction of EduKG. EduKG is publicly available at the following link: purl.org/edukg.
Data were collected in tabular format through a self-administered online survey whose content has been developed with the education experts who helped define the competency questions (see Section 2.1). Several French high schools and universities were asked to disseminate the survey among their students. A wide spectrum of geographical areas was targeted to ensure representativeness among survey respondents. From January 2022 to April 2022, 3,583 respondents have fully answered the survey and are part of EduKG.
Before constructing EduKG, some modeling choices were made, such as: • Students rated their curriculum on a 0 to 5 scale. Ratings were binarized so as to lead to the predicates likedCurriculum (rating ≥ 3) and dislikedCurriculum (rating < 3); • Students were allowed to provide keywords related to their educational interests. Because they were given a freeform text field to fill, consistency among respondents was needed. For instance, keywords that appeared too little were removed, and too precise keywords were replaced by broader concepts; • Publicly available datasets provided by government agencies were used to enrich the knowledge graph with additional curriculum information.
Then we use Python 3.9 to create RDF triples meeting the conditions specified by EducOnto. All these triples forms our knowledge graph EduKG. A comprehensive view of EduKG is provided in Table 1: the number of distinct entities for each intermediate class (Table 1.a), information about the number of Student-Curriculum interactions and the relative sparsity of EduKG (Table 1.b) and the total amount of entities, relations and triplets of EduKG (Table 1.c).
We will enrich EduKG by mapping EduKG keywords to DBpedia or Wikidata entities. This would give more genericity to EduKG and facilitate its use in downstream tasks. Similarly to EducOnto, we intend to extend EduKG by adding instances from other European countries, although its feasability will depend on privacy-related and data-sharing concerns. 4https://en.wikipedia.org/wiki/Bologna_Process #School Subjects #Majors #Specialties
15 92 13 #Interactions
7,021 #KG Triples 36,301
On a technical side, a public SPARQL endpoint will be provided, so that information can be retrieved by running SPARQL queries against EduKG.
Finally, recall that the ultimate goal for building EduKG was to provide a public dataset for
curriculum recommendation. Thus, the natural next step of this work is to design a RS that
specifically relies on EduKG, by refining the work initiated in [
This work is supported by the AILES PIA3 project (https://www.projetailes.com/) that seeks to support French students in their educational choices. We thank all the project partners: the University of Reims Champagne-Ardenne, the University of Lorraine, the Technological University of Troyes and the two Academies of Reims and Nancy-Metz.