B2728 - Language, Technology, Research III: Researching Language

Learning outcomes

The student knows the basic features (terms, concepts and methods) of empirical research on language. S/he is able to: identify a valid research problem in the area of bilingualism studies; formulate a well-grounded scientific hypothesis; choose an adequate method for solving the identified research problem; collect and appropriately analyze a set of empirical data; interpret and present the results in a coherent and scientifically sound manner.

Course contents

Research-oriented knowledge and skills are very much required in both university studies and the job market, and language-related professions are no exception. 

As a step towards gaining such knowledge and developing such skills, within this course the students will engage in a small-scale guided research project on the broad topic of bilingualism. The specifics are to be decided based on class size and individual/group interests. The starting point will be the recently completed PRIN project "UNIversally Inclusive Technologies to practice English - UNITE" (https://site.unibo.it/unite/en), which aimed to explore the applications of dialogue systems (i.e. chatbots) as tools for practising English as a Foreign Language. The activities will specifically focus on the UNITE corpus, a digital collection of learner-chatbot conversations, and on the analysis of the properties of these interactions.

Through this analysis, the course will lead the students to consider the following:

• how to approach language-related problems from a scientific perspective;
  
• how to link a generic question we have to a scientific theory and to an empirical setting;
• how to design and set up a research plan;
• how to conduct an empirical research study; 
• how to report on a study.

In addition, depending on the specific decisions taken, components covering specific phenomena and specific methods will be added.

The course constitutes the last part of the LANGUAGE, TECHNOLOGY, RESEARCH path; having previously attended the courses in Computational Thinking and Text Processing is helpful, but is not a requirement. 

Readings/Bibliography

Teaching and reading materials will be made available on the Virtuale platform.

Teaching methods

Frontal instruction will be combined with a seminar format. Project work will be based on student presentations and discussions including lecturer and peer feedback. Active participation by students is expected throughout the course.

Assessment methods

The assessment will be entirely based on the project, which will be assessed via in-class presentations during the semester (40% of the final grade) and a project report to be submitted in the regular exam sessions (60% of the final grade).

Class attendance is not compulsory, but given the project-based nature of the course, it is strongly reccommended. Students partially unable to attend due to overlaps with compulsory courses, as well as any non-attending students, should get in touch with the lecturer within the first two weeks of the semester.

Assessment scale

30-30L: Broad and in-depth knowledge of course contents, excellent ability to apply theoretical concepts in practice, excellent presentation skills, as well as excellent ability to analyse and synthesise.

27-29: Detailed and complete knowledge of course contents, good ability to apply theoretical concepts in practice, good ability to analyse and synthesise, good presentation skills.

24-26: Appropriate knowledge of course contents, fair ability to apply theoretical concepts in practice, fair presentation skills.

21-23: Appropriate but not detailed knowledge of course contents, partial ability to apply theoretical concepts in practice, acceptable presentation skills.

18-20: Sufficient but somewhat vague knowledge of course contents, basic presentation skills, uncertainties in the application of theoretical concepts in practice.

<18 (Fail): Fragmentary and superficial knowledge of course contents, errors in applying theoretical concepts in practice, poor presentation skills.

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The exam involves substantial use of AI. The quality of the final product remains, however, the responsibility of the student, who is expected to edit the work and ensure the accuracy of the content presented.

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Students with specific learning difficulties (SpLD) or with disabilities that can affect their ability to attend courses are invited to contact the University service for students with disabilities and SLD at the earliest opportunity -- ideally before the start of the course. The University service will suggest possible adjustments to the course work and/or exam, which must then be submitted to the course leader so they can assess their feasibility, in line with the learning objectives of the course. Please note that adjustments to the exam must be requested at least two weeks in advance.

Teaching tools

The materials needed for the course will be made available on Virtuale. In addition, students will have access to the materials produced within the UPSKILLS project. 

Office hours

See the website of Maja Milicevic Petrovic