Course Information
TitleΘΕΩΡΙΕΣ ΜΑΘΗΣΗΣ ΚΑΙ ΕΚΠΑΙΔΕΥΤΙΚΟ ΛΟΓΙΣΜΙΚΟ / LEARNING THEORIES & EDUCATIONAL SOFTWARE
CodeNET-06-03
FacultySciences
SchoolInformatics
Cycle / Level1st / Undergraduate, 2nd / Postgraduate
Teaching PeriodSpring
CoordinatorStavros Demetriadis
CommonNo
StatusActive
Course ID40002951

Programme of Study: PPS-Tmīma Plīroforikīs (2019-sīmera)

Registered students: 30
OrientationAttendance TypeSemesterYearECTS
GENIKĪ KATEUTHYNSĪYPOCΗREŌTIKO KATA EPILOGĪ635

Class Information
Academic Year2019 – 2020
Class PeriodSpring
Faculty Instructors
Weekly Hours4
Class ID
600155635
Type of the Course
  • Scientific Area
Course Category
General Foundation
Mode of Delivery
  • Face to face
Digital Course Content
Language of Instruction
  • Greek (Instruction, Examination)
Prerequisites
General Prerequisites
None.
Learning Outcomes
COGNITIVE Upon successful completion of the course, students will: 1) Describe the characteristics of modern scientific learning theories and their impact on educational software design. 2) Explain the functional and structural features of the various software categories (such as architecture, interface, etc.) and relate them to learning theories and corresponding didactical models. SKILLS Upon successful completion of the course, students will: 1) They can apply object-oriented programming techniques in Python programming language for software development in which ideas of lesson theory will be implemented (such as, learning simulation, smart algorithms for learning, etc.)
General Competences
  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Adapt to new situations
  • Work autonomously
  • Work in teams
  • Work in an international context
  • Work in an interdisciplinary team
  • Generate new research ideas
  • Design and manage projects
  • Be critical and self-critical
  • Advance free, creative and causative thinking
Course Content (Syllabus)
(a) Learning Theories Basic concepts and definitions: Cognition and Learning, Representation, Learning and Education, Learning Theories and Didactic Model, Technology and Technology-enhanced learning, Learning Theories and Educational Software, Behaviorism: Historical background, Operational conditioning, Impact on educational technology, Programmed instruction, "Drill 'n' Practice" software. Cognitive Theories: Historical background, Representational approach and Information Processing Theory, Cognitive theories and Educational Software Design, Cognitive Flexibility Theory, Double Coding Theory and Multimedia Learning. Constructivism, Historical background, Discovery/Inquiry Learning, Educational simulation software, Microworld and Modeller, Inquiry learning with simulation software. Social constructivism: Socio-cultural approach, Zone of proximal development, Computer-supported Collaborative Learning (CSCL), Learning Design Tools. Connectivism: Participatory Web 2.0, Social networks and learning/education, Wikis, Blogs and educational use, Mobile learning (m-learning). Constructionism: Experiential learning, Papert and Logo-like tools, Educational Robotics, Game-based learning (digital games, gamification). (b) Programming Lab Basic Python: Basic programming language features and execution model), Variables, Basic programming structures (if, while, for), Basic data structures (list, dictionary, tuple, set), Functions, File handling, Exception handling. Advanced Python: Object-oriented programming (classes and object/instances), Linking to external libraries, Multimedia interactive applications using the Pygame library.
Keywords
Learning Theory, Behaviorism, Cognitivism, Constructivism, Social Constructivism, Connectionism
Educational Material Types
  • Notes
  • Slide presentations
  • Multimedia
  • Book
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
  • Use of ICT in Laboratory Teaching
  • Use of ICT in Communication with Students
Description
Continuous and extended use of ICT: Use of course web site to communicate with students and disseminate learning material. Multimedia programming learning. Use of interactive Google forms for delivering online student quizzes to familiarize students with course content.
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures26
Laboratory Work26
Project30
Written assigments13
Exams3
Other / Others52
Total150
Student Assessment
Description
(1) Written exams to assess students' course conceptual knowledge. (2) Quiz Python to assess students' basic Python programming knowledge. (3) Project Python to assess students' advanced Python understanding and software development skills.
Student Assessment methods
  • Written Exam with Multiple Choice Questions (Formative, Summative)
  • Written Exam with Short Answer Questions (Formative, Summative)
  • Written Exam with Extended Answer Questions (Formative, Summative)
  • Written Assignment (Summative)
  • Labortatory Assignment (Formative, Summative)
Bibliography
Course Bibliography (Eudoxus)
>> «Θεωρίες Μάθησης και Εκπαιδευτικό Λογισμικό», Στ. Δημητριάδης, Εκδόσεις Τζιόλα, Θεσσαλονίκη, 2013. Από το 2016 το ανωτέρω βιβλίο είναι δωρεάν διαθέσιμο σε ψηφιακή μορφή μέσω του προγράμματος 'ΚΑΛΛΙΠΟΣ".
Additional bibliography for study
> Σημειώσεις του διδάσκοντος για την εκμάθηση της γλώσσας προγραμματισμού Python. > Κεφάλαια άλλων βιβλίων (διαθέσιμα σε ψηφιακή μορφή) και διαφάνειες μαθήματος.
Last Update
02-12-2020