Public Internet Data Mining Methods in Instructional Design,Educational Technology,and Online Learning Research

We describe the benefits and challenges of engaging in public data mining methods and situate our discussion in the context of studies that we have conducted. Practical, methodological, and scholarly benefits include the ability to access large amounts of data, randomize data, conduct both quantitative and qualitative analyses, connect educational issues with broader issues of concern, identify subgroups/subpopulations of interest, and avoid many biases. Technical, methodological, professional, and ethical issues that arise by engaging in public data mining methods include the need for multifaceted expertise and rigor, focused research questions and determining meaning, and performative and contextual considerations of public data. As the scientific complexity facing research in instructional design, educational technology, and online learning is expanding, it is necessary to better prepare students and scholars in our field to engage with emerging research methodologies.     

Some factors in problem solving: The name of the concept,specific instructions,and the ability to verbalize

Three extrinsic factors were manipulated in a problem-solving task performed by 80 10-year-old children of IQ 90–110. The factors were: The presence or absence of the name of the concept given in feedback, specific instructions, and whether the child was allowed to verbalize. The task was a modification of Vygotsky's experiment on concept formation using concrete material. The performance criteria were: time taken to solution, number of moves, and amount of verbalization. The presence of the name of the concept given in feedback speeded the time to solution and reduced the number of moves required. Specific instruction speeded the solution but did not reduce the number of moves; whereas verbalization reduced the number of moves but increased the time of solution.