Data Usage and Technology Applications

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For this week’s blog, I’ll be discussing data collection and assessment as it pertains to the ability of automated assessment applications to accurately measure satisfactory comprehension of varying concepts. Additionally, I’ll briefly discuss online cheating and academic integrity.

Big Data & Text Analysis Apps

With a growing reliance on data being used to shape educational endeavors and performance standards, the technology that compiles and measures that information is now to a point of sophistication where automated systems are capable of grading performance. However, the concern over reliability in assessment is a potential hurdle that shift towards relying on technology for automated assessment. According to Cope & Kalantzis (2016):

As is to be seen in unfolding developments in the field of technology-mediated writing and writing assessment, big data and education data sciences may in time offer learners, teachers, and researchers new windows into the dynamics and outcomes of learning, finely grained in their detail, varied in their sources and forms, and massive in their scope.  

In this regard, the data should assist informing the decisions of how instruction is designed, while relying less on bulk data without discernible applications.

Another aspect of this week’s discussion centers on computer-based text analysis paradigms. While the technological aspect of assessment and grading for written samples can be complex, there is extensive research to make those efforts accurate and timely; however the nuances of implementing software that can sufficiently measure the various factors that go into an act as subjective as writing can be almost as complex as the challenges of interpreting written text through technological applications.  

For myself, I might consider such applications when grading short-answer type questions, if only because the measure of course outcomes and the point worth of those questions have potentially less severe affects on learner performance when compared to entire essays.

In the future I would like to reference back to the following article that further elaborates on the challenges of big data and artificial intelligence:

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7604529/

Online Cheating

When considering online cheating and academic dishonesty, I am very interested in pursuing more information and research regarding lockdown browsers, especially since I may find myself using platforms such as Canvas and BlackBoard. In my experience, I feel that in higher education, plagiarism is a more common issue than exam or quiz-based cheating, but I look forward to learning more about curbing the likelihood of those instances and providing strict expectations of academic integrity.

Cope, B., & Kalantzis, M. (2016). Big Data Comes to School: Implications for Learning, Assessment, and Research. AERA Open. https://doi.org/10.1177/2332858416641907

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