Internship - Fuzzy Matching Methods for Improved
Il y a 2 mois
Televic Education
Televic Education is a world leader in the development of research-based solutions that help solve training, quality, certification, accreditation, permanent evaluation and collaboration challenges in many different industries.
The combination of our in-depth knowledge of specific sectors with scientific research has enabled us to create innovative solutions and to build long-term relationships with our clients that range from governments, educational institutions, hospitals as well as corporates.
Topic
assessmentQ is a digital exam platform developed and maintained by Televic Education. 250+ customers are using assessmentQ for organizing online and digital (practice) exams.
AssessmentQ offers automated correction tools to help teachers with the long and tedious task of manually correcting exam questions. However, most of these tools rely on predefined “correct” answers and do not consider spelling errors. To address this limitation, we propose indicating incorrectly spelled “correct” words as acceptable in exam contexts where spelling is not a priority (a form of fuzy matching). By doing so, we can provide more accurate and efficient grading while reducing the workload of teachers.
As part of this thesis/internship, your objectives are twofold: first, to investigate various fuzzy matching methods for incorrectly spelled words, such as character-based and phonetic approaches. Second, to create a working proof-of-concept that demonstrates the feasibility of fuzzy matching in improving automated correction.
Specifics
- Level: Academic Master, Bachelor, Master
- Specialty: IT, Machine Learning, IT, Software
- Type of work: Research: 30%, Implem.: 40%, Experim.: 30%
- Location: Televic, University
- Type of activities: Experimenting, Implementation, Literature study, Programming
- Number of students: 1 or 2
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