Abstract
More than 50% of the master curriculum in Graz Technical University consists of elective courses. All
the elective courses are provided with a more or less standard description that includes a number of
ECTS, a content of the course, knowledge and skills that students are supposed to get after successfully
accomplishing the course. The elective courses are combined into a number of catalogues and can be
freely selected by the student to form the particular study curriculum. Defining such a particular
curriculum is a challenging task since too many factors must be taken into account.
To help students with building a certain master curriculum, the special software application – so-called
curriculum assistant can be used. The curriculum assistant is a special component of the campus
management system that provides students with automatically generated variants of the curriculum. The
assistant randomly select courses from different catalogues until the desired number of ECTS is
reached. If the student is satisfied, the list of courses can be fixed and printed out, otherwise, the process
can be repeated as many times as needed. Obviously, the random process of the curriculum generation
must be sufficiently amended to get acceptable results.
Deep learning is the latest technology that considerably extends the opportunities for the machine
computing. Deep learning is successfully used in many areas such as image recognition and others.
Deep learning is expected to allow machines to solve problems in a manner similar to the human way
of thinking.
In this paper we describe an innovative application utilizing the deep learning technologies to facilitate
the curriculum assistant with a number of artificial intelligent features. Specifically, we introduce to the
curriculum assistant an evaluation of perspectives to get a particular type of job after the university.
Thus, the intelligent assistant infers the particular curriculum not only taking into account total number
of ECTS and necessary courses diversity but also - skills provided by the courses and current demand
for such skill on the recruitment market.
the elective courses are provided with a more or less standard description that includes a number of
ECTS, a content of the course, knowledge and skills that students are supposed to get after successfully
accomplishing the course. The elective courses are combined into a number of catalogues and can be
freely selected by the student to form the particular study curriculum. Defining such a particular
curriculum is a challenging task since too many factors must be taken into account.
To help students with building a certain master curriculum, the special software application – so-called
curriculum assistant can be used. The curriculum assistant is a special component of the campus
management system that provides students with automatically generated variants of the curriculum. The
assistant randomly select courses from different catalogues until the desired number of ECTS is
reached. If the student is satisfied, the list of courses can be fixed and printed out, otherwise, the process
can be repeated as many times as needed. Obviously, the random process of the curriculum generation
must be sufficiently amended to get acceptable results.
Deep learning is the latest technology that considerably extends the opportunities for the machine
computing. Deep learning is successfully used in many areas such as image recognition and others.
Deep learning is expected to allow machines to solve problems in a manner similar to the human way
of thinking.
In this paper we describe an innovative application utilizing the deep learning technologies to facilitate
the curriculum assistant with a number of artificial intelligent features. Specifically, we introduce to the
curriculum assistant an evaluation of perspectives to get a particular type of job after the university.
Thus, the intelligent assistant infers the particular curriculum not only taking into account total number
of ECTS and necessary courses diversity but also - skills provided by the courses and current demand
for such skill on the recruitment market.
| Originalsprache | englisch |
|---|---|
| Titel | EDULEARN19 Proceedings |
| Seiten | 7964-7967 |
| ISBN (elektronisch) | 978-84-09-12031-4 |
| Publikationsstatus | Veröffentlicht - 2019 |
| Veranstaltung | 11th International Conference on Education and New Learning Technologies - Palma, Mallorca, Spanien Dauer: 1 Juli 2019 → 3 Juli 2019 |
Konferenz
| Konferenz | 11th International Conference on Education and New Learning Technologies |
|---|---|
| Kurztitel | E-Learning19 |
| Land/Gebiet | Spanien |
| Ort | Palma, Mallorca |
| Zeitraum | 1/07/19 → 3/07/19 |
ASJC Scopus subject areas
- Informatik (insg.)