nagashima [at] cs.uni-saarland.de

NEWS

(July 2022) I successfully defended my dissertation with no required revisions!

(Nov 2021) Proposed my Ph.D. dissertation and I’m on the job market!!

(June 2021) Our ISLS paper was nominated for Best Design Paper!

(April 2021) We will be at ED Games Expo! Here’s our entry.

(Dec 2020) Won the Fred Mulder Open Education Practice Award!

(Nov 2020) Gave a talk at Keio Univ.

(Nov 2020) Won an AECT award!

(Nov 2020) Presented at OpenEd20

(Oct 2020) Made a tape diagram template (available under CC-BY-NC)

Seminars

Intelligent Systems for Supporting Human Learning (Block Seminar, Winter 2022/23)

Description: Intelligent systems (e.g., Intelligent Tutoring Systems) have been developed and empirically evaluated to support human learning in a variety of domains (e.g., STEM). Recent advancement in AI technologies has allowed such systems to become even more effective through, for instance, adaptive tutoring (e.g., adaptive problem selection, and affect detection). In this block seminar, we will review seminal papers on intelligent systems that support human learning and discuss future opportunities for further enhancing such systems. We will also pretend to be secondary-school learners and use some of the established intelligent systems to get hands-on experiences. The seminar is open to both master’s and bachelor’s students (but please note the different workloads assigned – see below). The seminar doesn’t require any prerequisite knowledge and is therefore open to students with any background, but knowledge or experience in one or more of these fields will help: cognitive science, learning sciences, and Human-Computer Interaction. This seminar is open to students in all departments across UdS campus. Active participation in class discussions and activities is expected and encouraged. I rather want to design learning experiences in this seminar with students – your active participation is the key to the success of the seminar! See more here. 

 

Learning objectives: By completing this seminar, you will be able to discuss the benefits and limitations of various intelligent systems designed for supporting human learning. You will also be able to generate new research and development ideas for the future of intelligent learning systems.

Lecturer:  Tomohiro Nagashima, nagashima@cs.uni-saarland.de 

The number of students:  12 maximum

Location:  Seminar room 0.16 in E1.3 Building (in-person attendance required; however, if remote participation is desired due to special reasons, please consult in advance)

Time: block seminar in the spring break 2023; we will meet 90-100 minutes on March 6 (Mon), 8 (Wed), 10 (Fri), 13 (Mon), 15 (Wed), 16 (Thu), and 17 (Fri).

Application for participation: When registering, please provide a short statement (1-2 paragraphs) on why you are interested in this seminar and any relevant experiences.

Grading (for Master’s students):

  • 15% daily reading posts (summary and your thoughts on papers we read)
  • 25% ITS critique (short critique report on a chosen system, only for master’s students)
  • 20% discussion lead (present 10min summary of the paper, once for every student)
  • 30% final report (1000-2000 words on the future of intelligent learning systems)
  • 10% contributions to discussion during the class

Grading (for Bachelor’s students):

  • 20% daily reading posts (summary and your thoughts on papers we read)
  • 30% discussion lead (present 10min summary of the paper, once for every student)
  • 40% final report (1000-2000 words on the future of intelligent learning systems)
  • 10% contributions to discussion during the class

Tentative List of Topics:

  • Day 1: Introduction, History of intelligent learning systems 
  • Day 2: Cognitive principles in designing intelligent systems for supporting learning
  • Day 3: Efficacy studies of intelligent tutoring systems
  • Day 4: Hands-on exercise (ITS for math learning)
  • Day 5: Open (student-suggested topic!)
  • Day 6: New technologies for learning with intelligent learning systems (e.g. VR)
  • Day 7: Future of intelligent learning systems