Giulia Di Giovanni & Jess Sirois

Article 1: Virtual Reality Can Support And Enhance Environmental Education

Summary

In their article, Jerowsky and Borda explain how the effective use of virtual reality (VR) and augmented reality (AR) can support and improve environmental education. Due to the distance, safety issues, financial constraints, and physical limitations, many locations that students are learning about are inaccessible. The authors claim that VR offers the opportunity to access places such as coral reefs and wetlands, allowing students to experience and study these ecologically sensitive areas. They explore multiple scenarios where VR or AR positively influences learning. Through their secondary research, Jerowsky and Borda conclude that VR and AR can empower students to contribute to solving the world’s complex environmental problems now and in the future.

Clark

Clark (1994) made many different claims regarding media and its influence on learning. His primary claim was that media are mediums that deliver instruction but that they do not influence learning. In Jerowsky and Borda’s article, the opportunity that VR and AR provide is evident. Students’ being able to study inaccessible ecosystems is revolutionary and avoids any harm to these ecologically sensitive areas. The one point Clark would make is the affordability argument. Clark (1994) states, “we must always choose the less expensive way to achieve a learning goal” (pp. 3).

Kozma

Kozma (1994), on the other hand, believed that “if there is no relationship between media and learning it may be because we have not yet made one” (p. 7). This appears to have evolved based on the successes of VR and AR studied in Jerowsky and Borda’s article. The results of the studies in their article show numerous positive outcomes, including increased knowledge retained by students, high levels of engagement, and significant improvements in problem-solving. Kozma (1994) claimed, “the task of the designer is to use the capabilities of the medium to create objects that generate interesting and effective conversations—-ones that influence learning” (p. 17). Jerowsky and Borda (1994) proved this to be true through their analysis of VR and AR in environmental education.

Article 2: Google’s Adaptive Learning Technologies Help Amplify Educators’ Instruction

Summary

EdTech magazine recently published an article on Google’s Adaptive Learning Technology, which leverages artificial intelligence (AI) to deliver robust learning designs to the classroom. According to the authors, the average U.S high school class has 30 students that require practice and personalized feedback in the classroom, which can be a burden to educators. The author claims that integrating Google’s Adaptive Learning Technology in the classroom might transform future education into a personalized learning experience. Using artificial intelligence in the classroom could assist educators in improving instruction, reducing administrative responsibilities, and providing meaningful feedback to their students, all while learners enhance their understanding of instructional ideas by obtaining immediate feedback and real-time support.

Clark

Clark (1994) might say that using artificial intelligence in the classroom would not provide the same learning outcome for all students. However, in Burrough’s article, she states that practice and immediate feedback have consistently been shown to be effective in modern classrooms. Adopting AI practice sets provides students with fast, personal feedback, which keeps them engaged and helps build confidence. If a student fails, they can receive immediate feedback to help verify they comprehend the topic before moving forward in a lesson. Clark (1994) might also claim that AI could not serve as a “unique cognitive effect for some learning task, then the attributes must be proxies for some other variables that are instrumental in learning gains” (pp. 2). According to Burrough’s article, using AI technology in the classroom can significantly improve a student’s feedback loop by allowing students to monitor their progress and accuracy when working on an assignment and providing them with additional beneficial content to help them learn. This statement implies that AI technology can have a cognitive effect on learners, which contradicts Clarks’ (1994) argument.

Kozma

Kozma (1994) might state that AI technology’s key benefit is enabling students’ cognitive processes through symbolic and processing capacities. Kozma (1994) defines symbol systems as a means of communicating information about a field of reference that includes written texts, photographs, maps, graphs, and so on (p.10). According to Burrough’s article, Google’s Adaptative Learning Technology provides students with visual explainers and videos. Furthermore, Kozma (1994) defines processing capabilities as a medium’s ability to work on available symbol systems in a particular fashion, allowing information to be received, organized, shown, saved, organized, and evaluated, among other processes (p.10). Burroughs (2022) highlighted this in her article by stating that practice sets use AI to distinguish identical responses, identify when students go off course, and discover other trends to better assist educators in seeing patterns and making corrections to enhance the students’ learning experience.

References

Clark, R. E. (1994). Media will never influence learning. Educational Technology Research and Development, 42(2), 21-29. https://doi.org/10.1007/BF02299088

Burroughs, A. (2022). Google’s Adaptive Learning Technologies Help Amplify Educators’ Instruction. Technology Solutions That Drive Education. https://edtechmagazine.com/k12/article/2022/08/googles-adaptive-learning-technologies-help-amplify-educators-instruction

Jerowsky, M., & Borda, A. (2022). Virtual reality can support and enhance environmental education. The Conversation. https://theconversation.com/virtual-reality-can-support-and-enhance-outdoor-environmental-education-183579

Kozma, R. B. (1994). Will media influence learning: Reframing the debate. Educational Technology Research and Development, 42(2), 7-19. https://doi.org/10.1007/BF02299087