COURSE UNIT TITLE

: DIGITAL TECHNOLOGIES IN MATHEMATICS EDUCATION II

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
LMÖ 4002 DIGITAL TECHNOLOGIES IN MATHEMATICS EDUCATION II COMPULSORY 1 2 0 4

Offered By

Mathematics Teacher Education

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR MELIKE YIĞIT KOYUNKAYA

Offered to

Mathematics Teacher Education

Course Objective

The aim of this course is to enhance the knowledge, skills, and critical thinking abilities of pre-service mathematics teachers and researchers in integrating Web 2.0 tools and AI-powered applications into mathematics instruction. The course includes hands-on experiences with interactive tools such as Desmos and GeoGebra, as well as the exploration of artificial intelligence technologies (e.g., ChatGPT, Photomath, Wolfram Alpha) and their use in mathematics education.

Learning Outcomes of the Course Unit

1   Identify Web 2.0 tools and use them effectively for pedagogical purposes in mathematics instruction.
2   Design interactive activities that model mathematical concepts using tools like Desmos and GeoGebra.
3   Recognize AI-supported mathematics applications and evaluate their use in the teaching process.
4   Develop learning materials that support mathematical thinking and problem-solving through digital tools.
5   Analyze the pedagogical and ethical aspects of using digital technologies in mathematics education.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction: Digital Transformation in Education and Mathematics
2 Overview of Web 2.0 Tools
3 Introduction to Desmos and Task Design
4 Introduction to Desmos and Task Design
5 Creating Interactive Simulations and Questions with Desmos
6 Creating Interactive Simulations and Questions with Desmos
7 Other Web 2.0 Tools: Kahoot, Padlet, Socrative, PhET
8 General review, course evaluation, midterm exam
9 AI in Mathematics Education: A Conceptual Framework
10 AI-Supported Applications I: Photomath, Wolfram Alpha, etc.
11 AI-Supported Applications II: ChatGPT, Khanmigo, etc.
12 Pedagogical and Ethical Discussions on AI in Mathematics Teaching
13 Designing Student-Centered AI-Supported Activities
14 Final Project Presentations (AI or Web 2.0-based activities)
15 Final Exam

Recomended or Required Reading

- Rezat, Hattenmann ve Peter-Koop (2014). Transformation A Fundemantal Idea of Mathematics Education. Springer.
- Afamasaga-Fuatai, (2009). Concept Mapping in Mathematics, Research into Practice, Springer.
- Orton (2004), Learning Mathematics, Issues, Theory and Classroom Practice, 3rd Ed, Continuum.
- NCTM (2009). Focus in High School Mathematics, Resoning and Sense Making.
- Common Core State Standarts for School Mathematics, (2010).
- MEB, Matematik Dersi Öğretim Programları (1-5. sınıflar), (6-8. sınıflar), (9-12. sınıflar), 2005, 2011, 2013, 2017, 2018.

Planned Learning Activities and Teaching Methods

Lecture, discussion, question-answer, observation, group work, preparing and presenting teaching practices

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 VZ Midterm
2 FN Semester final exam
3 BNS BNS Student examVZ * 0.40 + Student examFN * 0.60
4 BUT Make-up note
5 BBN End of make-up grade Student examVZ * 0.40 + Student examBUT * 0.60


Further Notes About Assessment Methods

Assignments and presentations, discussion, student reflection, and research proposal

Assessment Criteria

Assessment of students is measured by midterm and final exams in line with the learning outcomes.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

melike.koyunkaya@deu.edu.tr

Office Hours

Monday 13:00-15:00

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 1 13
Tutorials 13 2 26
Preparations before/after weekly lectures 13 3 39
Preparation for midterm exam 1 5 5
Preparation for final exam 1 10 10
Midterm 1 2 2
Final 1 2 2
TOTAL WORKLOAD (hours) 97

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14PO.15PO.16PO.17PO.18
LO.153
LO.2553
LO.353
LO.453
LO.5553