NB Language of instruction is English
In this course, students will:
1. Learn to implement data-based problem-solving / data-based instruction
2. Critically evaluate the research surrounding data-based instruction.
Data-based problem-solving (also known as data-based instruction) refers to the use of data to build effective educational programs for students with learning and behavioral difficulties.
The course begins with a description of a problem-solving approach to learning and behavior. A system-wide problem-solving model is described: Response to Intervention (RTI)/ Multi-tiered Systems of Support (MTSS).
The emphasis then turns to data-based problem-solving at the individual level, and the use of Curriculum-Based Measurement (CBM). CBM (in Dutch, Continue Voortgangsmonitoring, CVM) is a progress-monitoring system specifically designed for implementation of data-based problem-solving for individuals with learning difficulties.
Students will learn the skills and techniques needed to implement CBM within a problem-solving approach for an individual. Students also will critically evaluate the research on problem-solving approaches and CBM.
- Describe what a problem-solving approach is, the factors leading to a problem-solving approach, and the steps to problem solving.
- Describe a specific problem-solving model, Response to Intervention/Multi-tiered Systems of Support, and describe the potential advantages and disadvantages of such tiered systems of instruction.
- Describe Curriculum-based Measurement (CBM), a system for closely monitoring the progress of, and evaluating the effects of, instructional programs for individuals with learning difficulties.
- Critically evaluate and discuss the research on data-based problem-solving / data-based instruction and CBM progress monitoring.
- Implement data-based problem-solving.
- Prepare a data-based problem-solving report and present it to others in the class.
Mode of instruction
Lectures, presentations, and discussion.
Discussion questions and answers, presentations, and progress-monitoring project.
For the timetable of this course please refer to MyTimetable
Study material will consist of recent book chapters as well as primary research articles from leading journals in education, psychology, and cognitive science.
Recommended (and required for students in KLGO Master’s specialisation):
- Hosp, M.K., Hosp, J.L., & Howell, K.W. (2007). The ABCs of CBM: A practical guide to Curriculum-Based Measurement. New York: Guilford.
Brightspace will be used during the course.
It is mandatory to register for each course via uSis. This applies to both the lectures and the working groups, even if they take place online. Without a valid registration in uSis you will not be able to participate in the course and you will not have access to the Brightspace module of the course.
In addition, it is also mandatory to register separately in uSis for each exam (i.e. both the first exam opportunity and, if necessary, the resit) in uSis. This also applies to partial examinations in a course. This is possible up to 10 calendar days prior to the exam. You cannot take the exam without a valid registration in uSis.
NB If the exam concerns a paper or a practical assignment, you do not need to register in uSis.
Carefully read all information about the procedures and deadlines for registering for courses and exams.
The exam of this course is a paper. This means that you do not have to register yourself for this exam in uSis.
During this course Professor Espin holds offices one hour immediately after classes. She can also be reached by email.