UV9121 – Response Processes Data in Assessment
Course description
Course content
This course will introduce the participants to the contemporary uses of multi-modal process data across the assessment cycle, highlighting is strengths, and its challenges. The module will look at the uses of process data in test and survey design, and in the analysis of test engagement/affect, test performance, and validity.? We will take a deep dive into the theory and evidence of process data, including its uses in response model validation, and in evidence-centred designs.? Working through case study materials and a selection of the latest academic literature, we will explore and contrast the 'retrospective' uses of process data (i.e., data mining) with digital first uses 'by design' (i.e., process-oriented computational and adaptive designs).? We will also consider the implications of diversity in test-taking populations (educational, linguistic, neuro-developmental, and disability), and what that might mean for test equity,? fairness, and validity.
Learning outcome
The participants will gain a thorough understanding of the contemporary uses of process data in educational surveys and assessment, and the theoretical understanding of its strengths and weaknesses. They will also understand how process-oriented 'digital first' assessments make use of process data in adaptive test designs.
Admission to the course
This course has been developed?for?PhD candidates affiliated with the Faculty of Educational Sciences (UV), but others may also apply. PhD candidates at The Faculty of Educational Sciences will be given priority. As a minimum requirement, all participants must hold at least a Master's degree.
PhD candidates affiliated with the Faculty of Educational Sciences register through?Studentweb. Others may apply using the application form published at the current semester site.
Deadline for registering: please see the current semester site.
Other applicants can apply by filling out an electronic registration form on the corresponding semester page for the course.?
Recommended previous knowledge
Recommended completion of MAE4000 Data Science, MAE4011 Principles of Measurement, MAE4120 Item Response Theory or equivalent courses which provides necessary background knowledge.
Overlapping courses
- 1 credits overlap with UV9121U.
- 1 credits overlap with UV9917V4 – Micro-Analytic Methods in Large-Scale Educational Assessment (discontinued).
- 1 credits overlap with UV9917V4U.
Teaching
This course consists of on-site lectures and requires 80% approved attendance.?
You will find the timetable and the reading list on the semester site for this course.
The lectures are held by Professor Bryan Maddox.?
Examination
To obtain 3 credits, 80 % attendance, successful completion of the mandatory assignments and paper is required.
A more specific description of the mandatory assignments and paper will be given in the course.
Language of examination
The examination text is given in English, and you submit your response in English.
Grading scale
Grades are awarded on a pass/fail scale. Read more about the grading system.
More about examinations at UiO
- Use of sources and citations
- Special exam arrangements due to individual needs
- Withdrawal from an exam
- Illness at exams / postponed exams
- Explanation of grades and appeals
- Resitting an exam
- Cheating/attempted cheating
You will find further guides and resources at the web page on examinations at UiO.