The problem that I discussed with my peers was how to determine best-fit teaching technology for the higher education classroom. Digital literacy and technology are separately trained, supported, and assessed in Higher Education. Educators most often grab and use the newest and easiest technology available to serve their teaching and learning needs, yet fail to discern between appropriateness of the technology in accomplishing the established learning objectives. What if there was a taxonomy of technology that educators could use to select the right type of technology for the learning outcomes they were working toward? A technology taxonomy that would map to Bloom's Digital taxonomy enabling teachers to find the right digital tool for teaching would be an educational tool that can be utilized across disciplines. This educational tool would empower students’ success by reducing student overload with learning educational technology not directly connected to the content they are learning.
From coursework in Theories of Instructional Technology and Advanced Instructional Design, I am able to assess the theoretical approaches most relevant to this project including Information Processing Theory and Sweller’s Cognitive Load Theory. Sweller (1988) argues that novices of any discipline begin problem solving by analyzing surface structures for commonalities and use general means-end problem solving strategies. Digital Literacy novices practice simple forms of problem solving based on surface structures of navigation and recognition of technology from informal learning environments. However, digitally literate experts exhibit high cognitive loads and strong problem solving skills by focusing on the solution mode or outcome needs and select appropriate strategies and technology tools to achieve the goal (Sweller, 1988). Thus the learner builds on previous knowledge as they progress from surface problem-solving to deep problem-solving engaging both constructivism and cognitive load theory. I also favor Van Merriënboer’s Four Component Instructional Design for approaching complex problems. Van Merriënboer’s 4CID is a system model to develop complex professional skills. The model consists first of developing a learning situation composed from a combination of learning tasks and ordered sequentially according to the task's difficulty. Secondly, students are provided with supporting information as they practice solving complex problems due to increasing complexity of the task; however the level of the coaching and support will decrease as the learner progresses in performance. Thirdly, the increasingly complex learning environment is supported by the just-in-time information. The last stage is partial performance of complex skill set.
With mentorship, I am practicing systematic literature review as a discovery and analysis process. This process helps develop a rationale for why the problem exists, what has been researched or done about it, and what gap still exists. I am also able to discover seminal works, key journals and databases, and discover who is working currently on the solutions.
For quantitative methods, I favor Pearson’s correlation coefficient for measuring the relationship between categories. I learned about this in 6510 Research Statistics with Kellner looking at Digital Fluency in the Classroom: Recognizing Informal Digital Literacies of Young Adults. I would use the same method to analyze Bloom's taxonomy levels and a future categorization system for educational/teaching technology. In 6512 Analysis of Qualitative Research in Learning Technologies I used interviews and surveys to look at Minority Student’s Perception of Success: Is Language or Technology a Barrier?
I plan to partner and work through who takes lead for diverse parts of collaborative research. We work well by making two shared repositories: a Mendeley folder for shared research and a Google Doc for collaborative writing. In the first week, we will divide the research by main topics once we design the outline of the paper and collect, analyze, and recommend research to support our major ideas. We share recommended research for the literature review by starring the file in Mendeley and listing the hyperlinks to pdfs in the working outline. Then we will each take a section to write and alternatively edit the other’s drafts. In the second week, when there is data to gather, one of us will take lead on data and the other will take lead on graphics and analysis. The third week is for final revision. The conclusion is usually written by the author of the introduction to make sure they tie well together. The other author will write the abstract as needed.
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