1:1 Laptop Learning in Preservice Teacher Education
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This compilation paper was presented at the American Educational Research Association annual meeting in April 2011 at an invited session of the TACTL (Technology as an Agent of Change for Teaching and Learning) Special Interest Group.
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1:1 Laptop Learning in Preservice Teacher Education
- 1. Research Snapshots of 1:1 Computing in Preservice Education: 2002-2011 Joan E. Hughes, Hyo-Jin Helena Yoon, Nikki Wen, and Minwook Ok The University of Texas at Austin
- 2. Redesign of Program: Technology Content Courses Methods Courses Student Teaching In 2002, transforms to: TECHNOLOGY INTEGRATION
- 3. Evaluation Plan (2002-2007) CC: http://www.flickr.com/photos/jessandcolin/2518235070/
- 4. Current Research Plan (2008+) CC: http://www.flickr.com/photos/jessandcolin/2518235070/
- 5. Research Snapshot: Graduates 2003-2007 Helena Yoon presents a:
- 6. Instructors’ Use of Laptops
- 7. Students’ Use of Laptops
- 8. Highly Used Applications (<66%)
- 9. Research Snapshot: Graduates in 2008-2009 Joan Hughes presents a:
- 11. Predominantly draw on TPK
- 12. Moderate-High Digital Self-Efficacy (<3.0)
- 13. Strong Attitude toward LT (<3.2)
- 14. Communication (personal) – 60% daily/weekly
- 15. Web (personal) – 46% weekly
- 16. Productivity (educational) – 52% monthly
- 17. Creativity (pers/educ) – 38% monthly or lessCC: http://www.flickr.com/photos/jessandcolin/2518235070/
- 18. Number & Types of Evidence in Rationales: Fall 2008
- 19. Number & Types of Evidence in Rationales: Spring 2009
- 20. Research Snapshot: Cross-sectional Case Studies Helena Yoon presents:
- 21. Data Collection
- 22. Anticipated Findings Technology Attitude Technology Skills Technology Knowledge One to one Laptop infused Program
- 23. Research Snapshot:Special Education Certification Minwook Ok presents:
- 25. Pre- & Post survey
- 26. Observation
- 27. Interview
- 28. Lesson plan analysis
Notas do Editor
- This afternoon we would like to present a series of research snapshots into our longitudinal study of 1:1 computing in a preservice program. I already introduced myself, but also have 2 graduate students, Helena Yoon and Min Ok, who will co-present with me.
- In 2002 the preservice preparation program shifted from a standalone technology course to an approach that integrated technology throughout the program. This was enabled by Student laptop requirementFaculty development and buy-inOngoing learning for faculty and studentsOngoing technological support and accessTwo centers were established - one for technicalsupport and resources and one for faculty support, innovation, and training
- In the earlier research of the program, there was little formal research. One of the support centers conducted evaluation surveys of the students in 3 main areas: Laptop UseTechnology Use – in coursework, public school settings, and personallyUse of Support Resources
- In 2008, I began a research project that aimed to understand preservice teachers development within the program on 3 core areas: Knowledge and beliefs – Attitudes – self efficacy and learning technologiesAnd Behaviors – Current use / future useThese are captured, at this point, mostly through a pre and post survey. Digital Self-EfficacyLearning Technologies AttitudeTeaching Philosophy (construc. / directed)36 Technology ActivitiesStudents’ Frequency, Purpose, SkillInstructors’ UseSocial Networking: use, privacy, concernsSubject-specific technology use (current and future)Preparation for online teaching
- Using three semesters of data from our newest survey, we examined how preservice graduates were prepared to integrate technology in their future teaching. We examined students’ beliefs, attitudes, behaviors and knowledge.
- All scales run from 1-4We examined in depth open-ended answers to what they felt were their most valuable learning technology that they would use in the future if available. We found that productivity software and teacher-centric technologies dominated in their choices and that TPK was drawn upon most often to rationalize their choices.
- Visualization of Depth of Rationalization.
- Data collected from surveys, documents, observation and interview.