The COVID-19 pandemic forced dental education programs which had traditionally only ever been taught via face-to-face to pivot quickly to online instruction in order to complete the 20/21 school year. With this sudden shift to online learning, institutions re-evaluated the previously held notion that dental programs could never be taught online because students needed face-to-face instruction to develop the necessary competent clinical skills and communication skills to have close personal interactions with their patients.
In 2030, blended or hybrid dental programs have become the norm allowing dental students increased flexibility to do a portion of their studies online. However, moving to this model required learning institutions to rely on the steady, reliable nature of the Learning Management System (LMS) as described by Weller (2020) which led to the increased usage of learning analytics to inform decision making. With learning analytics, Pelletier et al., (2021) explain that institutions were able to harness the data to respond to student needs early by identifying those who exhibited low engagement or did not perform well on early assessments. By doing so, institutions were able to ensure that there was little to no attrition within cohorts. With the gathering of all this student data, issues arose of whether it was legal, ethical or both. Zijlstra-Shaw & Stokes (2018) state, “the issue of what is essential data for tracking learner performance and what is data captured because it is available and might be useful in the future presents an issue for the ethical and informed use of student data” (p. 659). By 2030, institutions had worked through some of the challenges faced early on with the push to blended or hybrid dental programs.
Although learning analytics has proven to be advantageous for the various stakeholders; ethical issues around transparency, data ownership and data interpretation had to be addressed when dental programs switched to a hybrid model. Initially, there was little transparency and lack of understanding regarding data collection from stakeholders. Pardo & Siemens (2014) argued that stakeholders should understand how the analytics process is carried out and stakeholders, specifically students should be informed of the type of information that is being collected; including how it is collected, stored, and processed. By 2030, dental institutions had created and implemented the necessary policies, protocols and procedures which raised student awareness about data collection so that students were in a better position to give their informed consent to data collection. With the increase in transparency along with better understanding, students were able to embrace and justify the use of learning analytics to their advantage by achieving their individual learning goals which in turn led to an increase in student retention in dental programs. In addition, Prinsloo & Slade found that (as cited in Zijlstra-Shaw & Stokes, 2018, e659) student trust and cooperation could be gained when there was an increase in the transparency of learning analytic activities.
Another challenge with learning analytics which needed to be addressed was the issue around ownership of the data. Pardo & Siemens (2014) proposed the student open model where transparency was increased because students were able to access and correct the data obtained about them. Prinsloo & Slade (2013) stressed the importance that institutions should not be the sole player with decision making power when it came to determining the scope, the definition and the use of educational data for learning analytics. Input from other stakeholders was required to make decisions. At one point early on in the shift to hybrid model, institutions considered that datasets could be collected from different dental schools and then pooled together for a larger dataset which could potentially be used for comparison purposes between provinces or countries. However, with the new policies in place and input from stakeholders, dental institutions ensured students had control of their data which included the ability to correct their data and institutions in turn would guarantee that students’ data were not going to be given out or shared with other institutions. By 2030, dental institutions needed to ensure that there were no 3rd party collectors of data involved in order to maintain the trust of their students.
A further challenge of learning analytics which had to be addressed by institutions was the interpretation of the data and the potential for profiling. In their study, Howell et al. (2018) reported concerns from academics regarding the potential to collect data which did not accurately reflect students’ activities. If that were the case, then how would dental instructors respond to the inaccurate interpretations which could potentially lead to the damage of a student’s self-esteem based on the inaccurate data. As well, early on many students were under the misapprehension that when their data was collected it was anonymous. However, as Holloway (2020) highlighted that advanced algorithms were easily able to pull personal and demographic information about individuals whose data had been collected from the vast abundance of data available. Institutions implemented policies which addressed both of these by taking the approach that more educational data did not always mean better educational data. In addition, institutions reassured students that these types of algorithms were not in use and that their identities would remain private and secure as part of their consent.
Learning analytics has proven to be advantageous for students, facilitators and institutions involved in hybrid dental programs in 2030. For students, they are able to track their own progress through the dental program and make improvements in their performance based on the interpretations and analysis of their data. Instructors are made aware of those dental students who are having challenges in the program and can review certain dental concepts if the data interpretation shows that students did not understand the concepts. Finally, institutions are able to ensure that there is little to no student attrition in the cohort and make changes to their programs to maintain student engagement. In order to gain acceptance from stakeholders, policies and protocols had to be created to address the challenges around transparency, data ownership and false interpretation of the data.
References
Holloway, K. (2020). Big Data and learning analytics in higher education: Legal and ethical considerations. Journal of Electronic Resources Librarianship, 32(4), 276-285.
Howell, J. A., Roberts, L. D., Seaman, K., & Gibson, D. C. (2018). Are we on our way to becoming a “helicopter university”? Academics’ views on learning analytics. Technology, Knowledge and Learning, 23(1), 1-20.
Pardo, A., & Siemens, G. (2014). Ethical and privacy principles for learning analytics. British Journal of Educational Technology, 45(3), 438-450.
Pelletier, K., Brown, M., Brooks, D. C., McCormack, M., Reeves, J., Arbino, N., Bozkurt, A., Crawford, S., Czerniewicz, L., Gibson, R., Linder, K., Mason, J., & Mondelli, V. (2021). 2021 EDUCAUSE Horizon Report Teaching and Learning Edition.
Prinsloo, P., & Slade, S. (2013, April). An evaluation of policy frameworks for addressing ethical considerations in learning analytics. In Proceedings of the third international conference on learning analytics and knowledge (pp. 240-244).
Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE review, 46(5), 30.
Weller, M. (2018). Twenty years of EdTech. Educause Review Online, 53(4), 34-48.
Zijlstra-Shaw, S., & Stokes, C. W. (2018). Learning analytics and dental education; choices and challenges. European journal of dental education: official journal of the Association for Dental Education in Europe, 22(3), e658-e660.