Lesley University DigitalCommons@Lesley Educational Studies Dissertations Graduate School of Education (GSOE) 2016 Steps Towards Personalized Learning Using Online Asynchronous Technology: A Study of 7th, 10th and 12th Graders at a Small Rural School in Massachusetts Michael Raymond Farmer Lesley University Follow this and additional works at: https://digitalcommons.edu/education_dissertations Part of the Online and Distance Education Commons, and the Secondary Education Commons Recommended Citation Farmer, Michael Raymond, "Steps Towards Personalized Learning Using Online Asynchronous Technology: A Study of 7th, 10th and 12th Graders at a Small Rural School in Massachusetts" (2016). Educational Studies Dissertations.edu/education_dissertations/3 This Dissertation is brought to you for free and open access by the Graduate School of Education (GSOE) at DigitalCommons@Lesley. It has been accepted for inclusion in Educational Studies Dissertations by an authorized administrator of DigitalCommons@Lesley. For more information, please contact digitalcommons@lesley.edu, cvrattos@lesley.
Running head: PERSONALIZED LEARNING AND THE ROLE OF TECHNOLOGY PERSONALIZED LEARNING AND THE ROLE OF TECHNOLOGY Steps Toward Personalized Learning Using Online Asynchronous Technology: A Study of 7th, 10th, and 12th graders at a Small Rural School in Massachusetts by Michael R. Farmer Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Lesley University April 12, 2016. ii PERSONALIZED LEARNING AND THE ROLE OF TECHNOLOGY Abstract Adolescents’ use of technology is an integral part of their lives. They use it for communicating, archiving, socialization, identity exploration, and a range of other purposes.
As a tool for adolescent academic learning, contemporary technologies target the brain’s recognition, strategic, and affective networks. Synthesizing adolescents’ affinity for technology with proven educational practices, knowledge of the brain’s workings, and an understanding of contemporary technologies’ capabilities, leads to the conclusion that technology-enabled personalized learning approaches can result in successful outcomes for students. This dissertation outlines findings from a 6-week mixed-methods study of 7th-, 10th-, and 12th-grade students attending a small rural school in Massachusetts. The purpose of this mixed-methods study involving 73 students was to discern from their perspective the efficacy of technology in facilitating more meaningful personalized learning experiences for students.
This purpose was accomplished within the framework of standards-based learning by exposing students to an asynchronous learning platform designed to support student learning. High adolescent affinity for technology translates into a desire for greater amounts of it in their learning experiences. Being in control of learning resonates affectively with adolescents, increasing their buy-in to their own learning. Technology features such as multimodality, online tools, feedback mechanisms, and the simple safety of an environment in which to experiment, provide enhanced learning experiences for many students.
In addition to content interaction, adolescents require interaction with teachers and peers, albeit to varying extents. Because students have different preferences across all the aforementioned dimensions, we need to adopt increasingly personalized approaches to learning, probably within blended learning iii PERSONALIZED LEARNING AND THE ROLE OF TECHNOLOGY environments. Technology can and must play a substantive role in delivering personalized learning experiences for all adolescents. iv PERSONALIZED LEARNING AND THE ROLE OF TECHNOLOGY Acknowledgements To the members of my doctoral committee, I extend my genuine appreciation for your support.
Sometimes support meant giving advice, sometimes holding firm to a view or idea. Sometimes support meant standing back, and sometimes support meant digging into the details. I could not have asked for a better doctoral committee – thank you for being the right kind of support at the right time: Dr. Linda-Mensing Triplett – Senior Advisor Dr.
Billie-Jo Grant – Committee Member Dr. Jo-Anne Hart – Committee Member To Dr. Dalia Khalil, it’s amazing how coffee shops in Cairo or Connecticut are equally good places to swap ideas and suggestions, thank you for your time and ideas. Paul Jablon, I benefitted greatly from those hours in Massachusetts coffee shops as well.
Thanks for sharing your knowledge and experience with me. v PERSONALIZED LEARNING AND THE ROLE OF TECHNOLOGY Preface In 2010, I had been teaching middle and high school students for about seven years. I had acquired an M. during that period and thought that as a reflective practitioner, I would eventually get this teaching thing figured out.
Then I read the book The Shallows: What the Internet is Doing to Our Brains (Carr, 2010). I began to speculate about some of the project- based, inquiry-driven, group-work oriented activities employed in my classroom. I wondered why they were not producing the learning results I believed students were capable of producing. Of course, the answer was clear! Whether I liked it or not, technology had changed young people somehow, and new ways of learning and teaching were needed—technology-based learning for a technology-oriented generation (Prensky, 2001).
I felt that if I learned a little more about how the adolescent brain worked, and if I understood a little more about contemporary technology, I could create a learning model that was in tune with the “how and what” that adolescents wanted. Then I would be able to help them achieve the learning outcomes I desired for them. I had a sense that technology was occupying a deeply emotional place in the hearts and minds of adolescents, that somehow it was a part of their lives in ways that no other medium had been in generations past. Fast forward to 2016: The journey taught me that my initial hypothesis was at best a little naïve.
In general, the adolescent generation embraces technology. They are adventurous with it, keen to use it, and in many ways have become highly dependent upon it (Boyd, 2014; Davies & Eynon, 2013; Sprenger, 2010). They use technology largely for entertainment, communication, photo albums, games, social networking, music, calendar applications, and location assistance. I have coined the term narcissistic technology to refer to this group of uses.
Two questions arise, however: Does the current generation’s comfort and adeptness with narcissistic technology vi PERSONALIZED LEARNING AND THE ROLE OF TECHNOLOGY extend in some way to academic learning? Can they learn to use technology to develop as life- long learners? In my classroom three years ago, I took the first step on a research journey. With the administration’s support, I reconfigured classes and created three heterogeneous seventh-grade social studies classes. Each of these classes was normally distributed with similar means based on pretest information. For a 12-month period, each of these classes was exposed to a different learning model.
One class operated under an essential-question, inquiry-driven philosophy in which students interacted freely with little restriction on information sources and student behaviors. The second class operated under a technology-driven/enabled model in which student instruction was largely based on a one-to-one relationship with the computer, accompanied by teacher interaction as required. The third model was a more traditional, teacher-driven classroom model. Although the individual sample sizes were too small to establish statistical significance using paired-samples t tests, the descriptive statistics were compelling.
The technology-oriented class produced the highest learning gains for students, and the interactive essential-question class produced the highest student satisfaction ratings. One thing was clear: The traditional classroom was not the best model to use. Also noteworthy was the fact that the greatest improvement in overall student satisfaction occurred for special needs students. Of the three special needs subgroups, students using technology made the greatest learning gains as well.
My takeaway from this research was that using technology materially improved the learning experience for students with special needs, and as a result, their overall satisfaction with school improved as well. The following year, all classes in seventh-grade social studies worked a minimum of 50% of the time with technology. This was a blended learning model. Using my own district- vii PERSONALIZED LEARNING AND THE ROLE OF TECHNOLOGY determined measure (Massachusetts Department of Education, 2013), I was able to establish the academic efficacy of this learning model by comparing improvement results on a standardized pretest/posttest model with the same results from the prior year’s traditional classroom group.
Although students enjoyed their online blogs containing all of their work, as opposed to the traditional classroom workbook or binder, I realized that the activities they engaged in were comparatively simple, limited-interaction, computerized versions of class exercises done in prior years. It did not help me to push the limits of my understanding about technology as a learning enabler for students. When I conducted a comparative study of several alternative schooling systems in the United States about 18 months ago, I was exposed to a number of state-of-the-art online learning platforms. I decided that a logical next step in my learning growth was to test the efficacy of an online platform within the context of its contribution toward better learning experiences for students.
This study represents a specific step toward what I now see as personalized learning. viii PERSONALIZED LEARNING AND THE ROLE OF TECHNOLOGY Table of Contents Abstract. vi List of Tables. xiii List of Figures.
xv Chapter 1: Introduction. 1 Background and Context. 3 Chapter 2: Literature Review. 12 Overview of the Literature Review.
12 The Brain-Based, Biological Basis of Learning. 21 The Role of Motivation and Emotion in Learning. 26 Motivation to learn. 26 Emotional and social needs of adolescents.
31 Universal Design and Technology for Personalized Learning. 39 Adolescent social and emotional uses of technology. 43 Delivering Personalized Learning. 55 Synchronous and asynchronous.
56 Motivation and interaction. 58 Gender and academic/technological efficacy. 60 Grade level/age. 63 Online learning and traditional classrooms, a blended learning solution.
64 Challenges in Implementing Technology-Based Learning Experiences. 68 The role of the teacher. 69 ix PERSONALIZED LEARNING AND THE ROLE OF TECHNOLOGY Implementing higher levels of technology in learning. 75 Chapter 3: Research Design, Methodology, And Analysis.
79 Presurvey and postsurvey. 81 Participants and Selection Criteria. 81 Student population and general environment. 83 The Execution of the Study.
85 The Edgenuity Platform. 87 Researcher Identity and Educational Philosophy. 94 Quantitative data treatment. 95 Qualitative analysis of survey data.
97 Assumptions and Limitations. 105 Confidence in using technology. 112 Descriptive statistics analysis. 112 x PERSONALIZED LEARNING AND THE ROLE OF TECHNOLOGY Inferential statistics analysis – ANOVA.
116 Inferential statistics – t tests. 123 Students’ own perceptions. 124 Edgenuity system data. 134 Summary of Observational Notes.
143 High school students. 146 Middle school students. 147 Summary of Selected Student Experiences. 152 Chapter 5: Discussion And Recommendations.
155 Summary of Research Findings. 173 xi PERSONALIZED LEARNING AND THE ROLE OF TECHNOLOGY Implications. 178 Areas for Further Study. 209 Appendix A: Letter Requesting Participation in Survey.
210 Appendix B: Informed Consent Permission. 213 Appendix E: Observation Rubric. 217 Appendix F: Coding Summary. 218 Appendix G: Tests of Normalcy.
219 Appendix H: Descriptive Statistics. 221 Appendix I: Summary of Codes. 224 xii PERSONALIZED LEARNING AND THE ROLE OF TECHNOLOGY List of Tables Table 1. Study Participant Summary.
Summary of Research Techniques. Average Number of Coded Responses by Subgroup. Code Groundedness Summary. Code Co-Occurrence Matrix.
Presurvey and Postsurvey Questions. RQ1—Summary Statistics. RQ1—Effect Sizes. RQ2—Analysis of Means.
RQ2—Questions 1-3: Students’ Confidence in Using Technology. RQ2— Questions 4-6: Students’ Perception of Ability in Using Technology. RQ2—Questions 7-10: Students’ Satisfaction in Using Technology. RQ2— Questions 11-16: Students’ Views on the Relevance of Technology.
RQ2— Paired Sample/Wilcoxon Results. RQ2—Independent Samples/Mann Whitney Results. 122 Table 18 RQ3—Summary of Statistics. RQ3—Summary of Grade Statistics.
RQ4—Summary of School Achievement Means. RQ4— Summary of Descriptive Grade-Level Achievement Means. RQ5—Summary of Statistics. 130 xiii PERSONALIZED LEARNING AND THE ROLE OF TECHNOLOGY Table 23.
RQ5—Frequency of Code Occurrences. RQ5—Selected Open-Ended Response Quotes. RQ6—Code Groundedness Summary. RQ6—Code Groundedness Comparison.
RQ6—Selected Open-Ended Response Quotes. RQ6—Negative/Developmental Open-Ended Response Quotes. Comparison of Completion Rate A. Comparison of Completion Rate B.
169 xiv PERSONALIZED LEARNING AND THE ROLE OF TECHNOLOGY List of Figures Figure 1. RQ3—Distribution of student responses. RQ4—Summary of academic performance. Levels of engagement.
High school student interaction. Middle school student interaction.