Table 2.
Entrants respond to dataset structure for providing university specialty recommendations.
| Question | Expected Value | Application |
|---|---|---|
| Desired study fields | Textual proposed definition of industry areas from the list | Identify the key interest fields to the entrant |
| Desired study subfields | One or more suggested text values from the list of subfields | Identify key interest subfields to the entrant |
| The main goal when choosing the specialty | Career opportunities; self-development; interesting academic process; opportunity to engage in a certain type of academic activity; formal need to obtain a degree | Understanding the entrant’s motivation further to improve the service and the appropriate selection of specialties |
| Expectations from the educational process at the university/department | Text data from the entrant. Optional field | Natural language processing (NLP) usage to find the most similar specialties according to the similarity score between their description, keywords, and the entrant’s expectations |
| Technician/Humanitarian preference ratio | A numeric value indicating the entrant’s preferable specialty focus | The selection of specialties depends on their ratio of humanitarian and technical focuses. Also, we can determine whether an entrant is interested in technical, humanitarian specialties, or an intersection of both. |
| Already selected specialties | Specialties the entrant has selected from the dropdown | Find alternative specialties and understand entrants’ motivation and interests. |
| Study format | Online/full-time/part-time | Selection of specialties according to the selected study format |
| Priority on state-funded education | Boolean value (True/False) | Selection and sorting of recommended specialties in descending order of admission probability to study on a state-funded form |
| Estimated budget for tuition per year/total tuition | A numerical value representing acceptable tuition for an entrant per specified period (term/year/multiple years) | Defining specialties that satisfy the entrant’s financial ability |
| Minimum/average/maximum scores in the current/latest educational institution (e.g., secondary school) on a national scale | Separate numerical values. For minimum and maximum scores would be good to provide subject names | Select the most relevant specialties following the success of education in primary school. It will also help determine how a specialty complexity level corresponds to the entrant’s knowledge level |
| Evaluation of the provided recommendations’ relevance | Relevant/Irrelevant OR a numerical value in a specified range | Define user satisfaction for the recommender system |