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Research: Alexa, How Can You Help Us? Exploring AI in the Classroom

Location: Posters; Level 3, Skyline Ballroom Pre-function, Table 42

Participate and share

Participate and share : Poster


Sunday, June 24, 7:00–8:30 pm
Location: Posters; Level 3, Skyline Ballroom Pre-function, Table 42

Melissa Anderson   Tonya Christianson   Amber Gaspar   Dr. Patrick Hales   Billi Jo Meyer   Beth Nelson   Krista Shilvock   Mary Steinmetz   Makenzi Timmons   Michelle Vande Weerd  
Voice assisted artificial intelligence (AI) is a rapidly growing household technolgoy market. Students are becoming increasingly more familiar with these devices, so education has to catch up. We'll share our experiences as classroom teachers integrating Amazon Echo into the classroom and how it affected the teaching and learning experience.

Skill level: Beginner
Attendee devices: Devices not needed
Focus: Technology infrastructure
Topic: Emergent technologies
Grade level: PK-12
Subject area: Language arts, STEM/STEAM
ISTE Standards: For Educators:
Learner
  • Set professional learning goals to explore and apply pedagogical approaches made possible by technology and reflect on their effectiveness.
Collaborator
  • Dedicate planning time to collaborate with colleagues to create authentic learning experiences that leverage technology.

Proposal summary

Framework

The Amazon Echo represents a growing field of voice assistants that use artificial intelligence to learn and respond to the needs of users. The Amazon Echo has an install base of over 19 million devices and a market share of 82% of the smart speaker market as of September 2017 (Morning Consult, 2017). From September 2016 until September 2017, there has been an 843% growth in the smart speaker market. Growth estimates suggest that by 2020, 75% of U.S. households will feature at least one smart speaker (Bryant, 2017). Numbers like these imply a need for investigation of this technology in a number of fields that extend beyond household use. This study will explore the notion that such a massively expanding area of the technology market must have implications and uses for education. Before jumping into the use of such new technology with education where there are consequences for learning environments and student outcomes, there must be a process and consideration of the value added.

One of the primary understandings of this research project is Technological Pedagogical Content Knowledge (TPACK) (Koehler & Mishra, 2009). Teaching requires a complex interaction of many understandings and specialized knowledge. Shulman (1986) proposed a framework for the intersection of essential understandings in teaching. This framework, Pedagogical Content Knowledge (PCK), suggests that teachers require a deep knowledge of pedagogy, how to teach, and content, what to teach, in order to be successful in instructing students. In addition, this framework indicates that, rather than such a simple dichotomy, excellent teachers grow an understanding of the intersection of pedagogy and content. Shulman states that this intersection, pedagogical content knowledge, represents a unique understanding of the ways in which certain content is taught and learned. In the modern context, technology is an inextricable area for understanding in educating students ready for the workforce. Koehler and Mishra (2009) took PCK and furthered it with the notion of technological knowledge as a separate understanding which interweaves with content, as technological content knowledge, and pedagogy, as technological pedagogical knowledge. Further, technology, pedagogy, and content coalesce as a total understanding of technological pedagogical content knowledge, or the understanding of how technology effectively integrates with the ways in which different kinds of content are taught and learned. This framework, TPACK, represents what we are striving for in this study; namely, we hope to further the technological pedagogical content knowledge of voice assistance artificial intelligence in the classroom. This growing technology presents the opportunity for new challenges and understandings.

Computers and other interfacing hardware have become a part of everyday life in the U.S. As a result, humans’ conceptions and perceptions of those interfaces have become more nuanced. Computers as Social Actors (CASA) (Lee & Nass, 2010) theory finds that people tend to apply social norms and expectations to computers as they would other people. This can be seen as people name their hardware, become excited at their presence, and become aggressive when they act against intention. Amazon Echo stands to further complicate that relationship as users can actually speak with the device. We perceive CASA as a fundamental component of understanding how Amazon Echo will interact with teachers and students in learning environments.

Methods

Eight teachers in K-12 public schools in the rural Midwest are utilizing Amazon Echo devices in their classrooms this year. They meet regularly to share their experiences and data collection. These teachers’ classrooms span from kindergarten to high school in the same rural Midwest school district. All of these teachers are members of a graduate level cohort. The data collection for this study can be thought of as occurring across two phases. In the first phase, these eight teachers will be keeping notes of their experiences with Amazon Echo, interviewing their students to ask about their experience, and trying out a variety of methods for using Amazon Echo. In the second phase, they will be reflecting on their experiences in a focus group and collecting student assessment data to look for any increased learning outcomes. The table below represents the grade levels and content areas of the eight classrooms in this study.

Case 1: Elementary, Kindergarten
Case 2: Elementary, Kindergarten
Case 3: Elementary, 3rd Grade
Case 4: Elementary, 4th Grade
Case 5: Elementary, 4th Grade
Case 6: Middle School, 6th Grade English
Case 7: Middle School, 8th Grade Science
Case 8: High School, German I, II, and III

This research study will follow tenets of case study research (Stake, 1995). With such a broad spectrum of experiences represented, it is important to understand the nature of each of the eight classrooms. In that way, there are eight stories that comprise the bounded system of this case study. In specific, the bounded system is the implementation of Amazon Echo in the classrooms among these eight teachers, yet that will play out differently in each of those classrooms. Data analysis will involve these teachers dissecting their experiences and the experiences of their students to tell the story of their classroom. As an additional phase of data analysis, each teacher within the cohort will share their story and search for commonalities and differences. Those commonalities and differences will comprise the findings of this research. Stake (1995) upholds that case study research comprises multiple methods of data collection and analysis, and that those methods should fit the bounded case. In the context of this study, teachers will be conducting the data analysis and collection needed to tell their story to colleagues and the reach findings through discussion and focus grouping.

Results

The data collection and analysis phase of this research is on-going as of the submission of this proposal. Data collection and analysis will conclude in spring 2018. With that in mind, we have a few preliminary results and some expectations in these early stages. First, we expect to use the data analysis to explore the nature of student and teacher engagement with the Amazon Echo and if that engagement led to better student outcomes. One of those outcomes we’re particularly interested in examining is if being able to use Amazon Echo to ask questions effects the ways and types of questions students ask. Also, Amazon Echo might change the nature of inquiry of students; rather than typing out questions on search engines, students can simply ask the questions verbally. If that occurs regularly in classes, that might have an effect on the classroom experience. Additionally, the CASA theory might play out in these classrooms. In what ways will students form a relationship with the Amazon Echo as they have to interact with it on a daily basis? Some initial conversations indicate that students develop a sort of anticipation for “Alexa” and want to interact or at least hear the teacher interact. We hope to coalesce some of these findings to further the conversation about using this growing innovation in the classroom. The outcome of this project should be information that other teachers thinking about using voice assisted artificial intelligence in the classroom will find valuable to increase their technological knowledge.

Importance

Voice assisted artificial intelligence is a significant area of technological innovation (Bryant, 2017). As a result, students in K-12 schools should be exposed to and experiment with this burgeoning technology. In addition, this type of technology may provide opportunities for enhancing classroom experiences and student engagement. Within a TPACK framework (Koehler & Mishra, 2009), it is essential to first have a technological understanding before trying to figure out how to connect that understanding with how it might impact pedagogy and content, or how it might integrate into teaching and learning effectively to enhance the experience rather than just becoming a tertiary feature in the room as many hardware innovations do. This smart speaker trend, and the focus on voice assisted artificial intelligence, does not seem poised to decrease. Amazon has a division of 5,000 employees devoted solely to the development of its Echo device (Vanian, 2017). That kind of investment suggests more to come. This means education must adapt and evolve alongside this emerging part of the day-to-day technology of the world and workplace or risk falling behind.

References

Bryant, S. (2017, August 30). Smart speaker market grows. Futuresource Consulting. Retrieved from https://futuresource-consulting.com/Blog-Post.asp#post3081

Koehler, M. J., & Mishra, P. (2009). What is technological pedagogical content knowledge? Contemporary Issues in Technology and Teacher Education, 9(1). Retrieved from http://www.citejournal.org/volume-9/issue-1-09/general/what-is-technological-pedagogicalcontent-knowledge

Lee, J.-E.R. & Nass, C.I.. (2010). Trust in computers: The computers-are-social-actors (CASA) paradigm and trustworthiness perception in human-computer communication. Trust and Technology in a Ubiquitous Modern Environment: Theoretical and Methodological Perspectives. 1-15. 10.4018/978-1-61520-901-9.ch001.

Morning Consult. (2017). National Tracking Poll #170603 [Data file]. Retrieved from https://m orningconsult.com/wp-content/uploads/2017/06/170603_crosstabs_Brands_v3_TB-1.pdf

Shulman, L. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 15(2), 4-14.

Vanian, J. (2017, September 27). Amazon has a stunning number of people working on Alexa. Fortune. Retrieved from http://fortune.com/2017/09/27/amazon-alexa-employees/

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Presenters

Melissa Anderson, Brookings School District
Tonya Christianson, Brookings School District
Amber Gaspar, Manchester Park Elementary
Dr. Patrick Hales, South Dakota State University
Billi Jo Meyer, Brookings School District
Beth Nelson, Brookings School District
Krista Shilvock, Mickelson Middle School
Mary Steinmetz, Brookings School District
Makenzi Timmons, Brookings School District
Michelle Vande Weerd, Brookings School District

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