Recommended Courses and Programs of Study

If you’re interested in research that impinges directly on CS education, here are some recommended courses and programs in the Graduate School of Education and the School of Information you may want to consider.

Programs and Certificates

Relevant iSchool Courses

  • INFO 247: Information Visualization (4). The design and presentation of digital information. Use of graphics, animation, sound, visualization software, and hypermedia in presenting information to the user. Methods of presenting complex information to enhance comprehension and analysis. Incorporation of visualization techniques into human-computer interfaces.
  • INFO 216: Computer-mediated Communication (3). (Formerly INFO 290-12) This course covers the practical and theoretical issues associated with computer-mediated communication (CMC) systems (e.g., email, newsgroups, wikis, online games, etc.). We will focus on the analysis of CMC practices, the relationship between technology and behavior, and the design and implementation issues associated with constructing CMC systems. This course primarily takes a social scientific approach (including research from social psychology
  • INFO 256: Applied Natural Language Processing (3). Enrollment may be limited to graduate students and/or iSchool students. This course examines the state-of-the-art in applied Natural Language Processing (also known as content analysis and language engineering), with an emphasis on how well existing algorithms perform and how they can be used (or not) in applications. Topics include part-of-speech tagging, shallow parsing, text classification, information extraction, incorporation of lexicons and ontologies into text analysis, and question answering. Students will apply and extend existing software tools to text-processing problems.
  • INFO C263: Technologies for Creativity and Learning (3). How does the design of new educational technologies change the way children learn and think? Which aspects of creative thinking and learning can technology support? How do we design systems that reflect our understanding of how we learn? This course explores issues in designing and evaluating technologies that support creativity and learning. The class will cover theories of creativity and learning, implications for design, as well as a survey of new educational technologies such as works in computer supported collaborative learning, digital manipulatives, and immersive learning environments.
  • INFO 271: Quantitative Research Methods for Information Systems and Management. The goal of this course is to provide students with an introduction to many different types of quantitative research methods and statistical techniques. This course will be divided into two sections: 1) methods for quantitative research and, 2) quantitative statistical techniques for analyzing data. We begin with a focus on defining research problems, theory testing, causal inference, and designing research instruments. Then, we will explore a range of statistical techniques and methods that are available for empirical research. Topics in research methods include: Primary and Secondary Data Analysis, Sampling, Survey Design, and Experimental Designs. Topics in quantitative techniques include: Descriptive and Inferential statistics, General Linear Models, and Non-Linear Models. The course will conclude with an introduction to special topics in quantitative research methods.

Relevant Education Courses

(Courses labeled “SCMATHE” internally, for “Science and Mathematics Education”, are GSE courses, and are listed as EDUC course numbers by the campus. SCMATHE identifies the course as affiliated with the SESAME program in the GSE.)
  • EDUC W161: Design of Digital Learning Environments. (3) Digital learning environments are taking residence in the educational experience of many, from replacing components of traditional classroom instruction to providing open platforms for lifelong learning. In this class we will study the various forms and functions of a sampling of digital learning environments ranging from subject specific Intelligent Tutoring Systems in K-12 to domain neutral systems for post-secondary online learning.
  • EDUC 224A: Mathematical Thinking and Problem Solving (Schoenfeld) This course explores contemporary research on mathematical thinking, teaching, and learning, with a particular emphasis on “higher order thinking skills” and problem solving – what are they, and what kinds of classroom environments promote their development. That emphasis is placed within the larger question of, “What are the properties of powerful learning environments – environments from which students emerge as knowledgeable and resourceful thinkers and problem solvers?” The course offers a combination of readings, hands-on activities, and a capstone project. Offered Fall semester of odd-numbered years,
  • EDUC 224B: Paradigmatic Didactical Mathematical Problematic Situations (Abrahamson) Paradigmatic Didactical Mathematical Problematic Situations are contexts for collaborative inquiry into the practice, epistemology, and pedagogy of mathematics. Building on the Learning Sciences literature, the course creates opportunities for students to engage in interesting mathematical problems from secondary-school content. Final projects include design, implementation, and analysis of a lesson. Offered Fall semester of even-numbered years.
  • EDUC 210. Practicum in Science and Math Education Research and Development. (1-4) [a/k/a “Seminal papers in education”. Take in Fall, since Spring format is for Education MS students to work on their MS projects] Course may be repeated for credit. One unit of credit for each four hours of student effort per week. Two hours of meeting per week. Prerequisites: Consent of instructor. Practical experience on an educational research or development project on campus or elsewhere for 8-12 hours per week. Class meetings augment research experience with discussions of readings and interaction with guest speakers.
  • (SCMATHE) EDUC 220C: Instructional Design in Science and Mathematics Education: Designing Educational Technologies (Linn) In this project-oriented course students, individually or in small groups, are mentored to design and refine an innovative use of technology in any topic area to strengthen teaching and learning in college, precollege, laboratory, or informal contexts. Students explore theories of instruction; design of assessments; issues in diversity, gender-equity, and accessibility in design of technologies for learning. Offered Spring semester in odd numbered years.
  • DATA 144: Data Mining and Analytics (Pardos) Data Mining and Analytics introduces students to practical fundamentals of data mining and emerging paradigms of data mining and machine learning with enough theory to aid intuition building. The course is project-oriented, with a project beginning in class every week. The in-class portion of the project is meant to be collaborative and a time for the instructor and GSIs to work closely with project groups to understand the objectives, help work through software logistics, and connect project work to lecture. Lectures will introduce theories, concepts, practical contexts, and algorithms. Students should expect to leave the class with hands-on, contemporary data mining skills they can confidently apply in research and industry. The final project may serve as the beginnings of a Master’s thesis.
  • EDUC 274A:  Measurement in Education and the Social Sciences I. (Wilson) Students design an instrument, investigate its measurement properties (specifically, validity, and reliability), and refine it. The act of measuring is positioned as a link between qualitative observations and quantitative measures. Design of instruments is discussed in a variety of contexts, such as interviewing, standardized testing, and performance assessment. We will discuss both classical and modern testing approaches from conceptual and practical points of view.
  • EDUC 224B: Design-Based Research Forum (Abrahamson) (offered Fall) A design-build-implement-analyze-theorize-publicize practicum forum for participants to first learn about design-based educational research work and receive support in their original and on-going projects. Following several orientation weeks, in which we discuss fundamental resources and participate in hands-on activities, subsequent readings are customized to individual students. The course culminates with presentations, and students submit an empirical research paper: Propose a conjecture about how students could (better) learn some concept or skill, build/deploy a prototype aimed at helping students learn that concept or skill, observe, refine, repeat. Here are some example past projects. This is one of the courses from the Embodied Design Research Laboratory.
  • EDUC 295B. Technology, Curriculum, and Instruction. (3) Three hours of seminar per week. Formerly Education in Mathematics, Science, and Technology 291B. To explore the cognitive consequences of technology in instruction and learning, the promise of technology in education will be examined, and exemplary instructional software will be explored. A model of knowledge acquisition and knowledge change incorporating technological delivery of instruction will be developed.