Intelligent Systems and Human Learning

Intelligent Systems and Human Learning (new advanced lecture)

 

If you are already registered but may not take this course, please drop it as soon as possible so that students on the waitlist can join!

If you were not able to register for this course on CMS but want to take, please email one of the teaching team members (below) so that we can add you to the waitlist.

Description:

This new advanced lecture introduces various kinds of advanced technologies used to support human learning (e.g., in school classrooms). We will review and critically analyze different techniques, learning science theories and principles, “learning engineering” efforts, and technological features that are embedded in such systems. We will work on a group-based project in which students will conduct an end-to-end cycle of designing, implementing, and testing (via a small experiment or user study) a learning technology for a target goal/domain, guided by the instructor and teaching team. The goal of this lecture is to help you understand core technologies and approaches used in the design/development of learning technologies (that are used in practice) and to equip you with the skills of designing/developing a piece of learning technology and evaluation its effectiveness on learning, engagement, and on related constructs. The course welcomes students with any background (in terms of cultural, racial, disciplinary, and technological) -- we are looking forward to building a community of learners with diverse perspective to engage in deep discussions and hands-on activities.

 

The first session is on Tuesday, April 15th! See you all in HS002 at 12:15!

 

Lecturer:  Prof. Dr. Tomohiro Nagashima (https://tomonag.org/) 

Teaching Assistant: Dr. Man "Echo" Su (https://www.mansu.net/)

Tutors: Marjo Toska & Ida Amoli

Location and Time: Tue 12:15-13:45 HS002 for the lecture; Thu 12:15-13:45 HS003 for the tutorial (both in E1 3 Building)

Credit points: 6CP

 

Learning objectives:

In this course, students will be able to

• 1) critically analyze learning technologies using learning sciences principles
• 2) learn how to conduct a user-centered design and development of learning technologies, and
• 3) learn how to empirically test the effectiveness of learning technologies through multiple methods.

 

Prerequisite:

Students will be designing and developing a learning technology (as a group). Therefore, students are expected to have web-based programming skills and experience. Students will also be testing a learning technology with stakeholders in the form of an experiment or user study – some basic knowledge of HCI, user studies, experimental design is a plus but not required. The course is open to any students from any department/faculty/degree programs across campus.

 

Course structure and schedule:

For most of the weeks during the semester, we will follow the structure below, where we'll begin with reading 2-3 assigned papers, attend the lecture, then tutorial, and submit the assignment by the end of week (there are several weeks without assignments).

Monday	Tuesday	Wednesday	Thursday	Friday	Saturday	Sunday
Readings due	Lecture		Tutorial			Assignment due

Detailed, week-by-week course topics and readings will be shared in the course Teams space, but here are the main topics that will (most likely) be covered in the lecture:

• Complexity of human learning
• Cognitive task analysis
• Established instructional strategies that are used in adaptive learning software 
• Intelligent tutoring systems
• Cognitive and student modeling, knowledge tracing
• Teachable agents and pedagogical agents
• Data-informed system improvement
• Conversational agents
• Generative AI for teaching and learning
• Multimodal learning technologies
• Participatory design
• User studies (UX methods) and experiments

 

Grading:

10%: Weekly reflection posts

20%: Mid-term exam/quiz

30%: Weekly individual and group-based assignments

20%: Final project report (individual)

20%: Final presentation (group)

5% bonus: Contributions to discussion during the class

*No final exams are planned 

Core activities:

• Attending and engaging in lectures: students are expected to attend the in-person lecture every Tuesday. Lectures (led by Tomo, occasionally Echo) introduce important key concepts, examples, and core knowledge and skills needed to understand the topic. Typically, lecture sessions would start with a 40-50min lecture, followed by hands-on activities to contextualize lecture concepts through tangible learning experiences.
• Attending and engaging in tutorials: students are expected to attend the tutorial sessions every Thursday. Tutorials, led by our teaching assistant and tutors (Echo, Ida, and Marjo) help students better understand key concepts covered in the lecture during the same week, with examples and activities, as well as some extra resources. Tutorials also provide important opportunities where students can spend time working on assignments and individual/group projects and receive help from the teaching team.
• Weekly reflection posts: For every week (except for the first week), students are asked to read 2-3 research papers (not very long) and share a paragraph or two on their thoughts on the papers -- these thoughts can be deep questions, comments relating what they read with their own experience, and/or critiques. Weekly reflection posts help you deeply engage with the readings (which give basic knowledge of the topic covered in the week), which will help you better understand the lecture and tutorial sessions.
• Mid-term exam: In around Week 7 or 8, we will be conducting an open-book mid-term exam, where you will be applying concepts learned in the first half of the lecture to answer some deep questions about intelligent learning systems. More details to come!
• Individual assignments: In the first half of the lecture, to scaffold students' contextualized understanding of key concepts and to help students practice skills, we will assign a series of small assignments.
• Group project: After getting familiar with key concepts, skills, and knowledge, which students practice through several small assignments in the first week, during the second half, students will work on a group project in which they will design, develop, and test a learning technology.
• Final presentation/demo: At the end of the lecture, we will have a "demo day" where all the groups will showcase what they have done for the group project.
• Final report: Students will individually reflect on what they have learned in the course and what are some things that they would have done differently.

 

Readings:

TBD: All readings will be provided .

 

Attendance Policy:

All the lectures and tutorial sessions will be held in-person in the lecture halls (unless otherwise specified). Therefore it is expected that students come and attend the classes. We will share lecture materials after each class, but no recordings will be shared. This means that students can miss a few classes but it is their responsibility to catch up with the content and assignments.

 

Contact policy:

To streamline the process of answering student questions, we have decided the contact policy as follows:

Students should contact one of the teaching team members (see below to find who you should contact) for all the questions via Teams (no emails). The contact person will then respond to you and/or consult with other team members to get the answer and deliver it to you. Please do not contact other team members, as it won’t be answered if you contact those who are not the primary contact specified below!

Week 1(14.04) – Week 6 (23.05): Echo

Week 7 (24.05) – Week 11 (27.06): Marjo

Week 12 (28.06) – Week 16 (01.08): Ida

Also, if you have any questions about course registrations etc., please contact Study Coordination (studium@cs.uni-saarland.de ) directly as we cannot help much.

 

Generative AI policy:

Understanding how and when to use generative AI tools is an important skill for future professions. To that end, you are welcome to use generative AI tools in this class as long as it aligns with the learning outcomes or goals associated with assignments. You are fully responsible for the information you submit based on a generative AI query (such that it does not violate academic honesty standards, intellectual property laws, or standards of non-public research you are conducting through coursework). Your use of generative AI tools must be properly documented and cited for any work submitted in this course (e.g., AI-generated text appears in a different colored font, quoted directly in the text, or use an in-text parenthetical citation).

 

Food policy:

You are welcome to bring in your own food to eat and drink during the sessions. We don’t mind seeing students eating in the class, as long as your classmates don’t mind.

 

Notes on respecting teaching team's (and each other's) time:

Every one of you have a busy schedule - balancing coursework, research jobs, tutor jobs, and other responsibilities - and that's same for us. While we try to answer all the questions within 24 hours, we kindly ask you to understand that our response to your questions may sometimes be delayed, especially on weekends. 

 

Accommodations for learning needs and the importance of inclusion:

If you have any needs that require some adjustments for you to succeed in this lecture, please discuss with Tomo in advance, and/or contact the Equal Opportunities and Diversity Unit at UdS: https://www.uni-saarland.de/en/administration/diversity.html Also, it is important that all members of the class feel respected, safe, and valued. I recognize that my ideas and thoughts might be biased based on my training and cultural experiences. Therefore, please let me know if at any time you feel uncomfortable in the class (this also applies to course materials and discussions we will have in the class).