Fledgling Thoughts on AI from Four Assigned Readings

Photo Credit: Tierney – stock.adobe.com

The following shares some thoughts on AI in education, based on four scholarly papers on the subject. The content below includes two big ideas that stood out per article, followed by one(ish) sentence on how it changed what I think of AI or how it might apply to my work. Truth be told, I have done very little reading in AI, so nearly everything I read changed my thinking. The reflections below represent the concepts that stood out most to me.

Nemorin, S., Vlachidis, A., Ayerakwa, H. M., & Andriotis, P. (2023). AI hyped? A horizon scan of discourse on artificial intelligence in education (AIED) and development. Learning, Media and Technology, 48(1), 38-51.

SUMMARY: The study seeks to understand how the AI ecosystem might be implicated in a form of knowledge production which reifies particular kinds of epistemologies over others. Using text mining and thematic analysis, this paper offers a horizon scan of the key themes that have emerged over the past few years during the AI-Ed debate.

  • The following is a paraphrase quote: Despite the consensus that AI should be ethical, there is also disagreement on what comprises ethical AI, and the ethical criteria, technical standards and protocols necessary for its implementation. The values attached to many of these systems are deeply intertwined with the values of those who create it, and these do not necessarily translate to all contexts and traditions.  “Intercultural information ethics scholar Pak-Hang Wong warn, there is a danger of dominance of ‘Western’ ethics in AI design … to the exclusion of other value systems…” With this in mind, when AI systems designed external to the context within which they are to be introduced, whose knowledge becomes privileged when said systems are fully integrated into education through policy and practice? This is especially important when thinking about education as a space where ideology is passed down (IEEE, 2018).
    REFLECTION: Whose knowledge is privileged? Terrifying?
  • First of all is the design and development of resources to use education as a tool to contribute to poverty reduction. This trend hinges on the importance of education for national economic growth and development. …Education is not only seen as a good that nurtures human flourishing; it is also a sector from which significant profits can be made, especially in light of innovations in AI educational technologies. AI in education is a path to a global market share valued at US$1.1 billion in 2019, and it is expected to reach US$6 billion in 2024 and US$25.7 billion by 2030 (Holmes et al, 2021).
    REFLECTION: AI in education is driven by market interests, and the development of STEM skills has been tightly connected by many major international organizations to economic dominance. Why does this surprise me?

Sofia, M., Fraboni, F., De Angelis, M., Puzzo, G., Giusino, D., & Pietrantoni, L. (2023). The impact of artificial intelligence on workers’ skills: Upskilling and reskilling in organisations. Informing Science: The International Journal of an Emerging Transdiscipline, 26, 39-68.

SUMMARY: This paper aims to investigate the recent developments in research and practice on the transformation of professional skills by artificial intelligence (AI) and to identify solutions to the challenges that arise. Excellent paper!

  • Before 2030, it is estimated that 375 million people (14% of the global workforce) may need to change jobs due to AI-related technological advancements. It is estimated that the share of key skills will change by 40% in the next five years, and 50% of all workers will need retraining and further education (World Economic Forum (WEF), 2020). Key skills that are expected to increase in importance by 2025 include technical skills critical for the effective use of AI systems and soft skills (also called transversal skills) such as critical thinking and analysis, problemsolving, and self-management (WEF, 2020).
  • Artificial General Intelligence: AGI could have a significant impact on human skills in organisations, either by automating tasks currently requiring human intelligence and problem-solving abilities and the need for workers to acquire new skills or retrain for different roles, or by augmenting human intelligence and problem-solving abilities, leading to increased productivity and efficiency, as well as the opportunity for workers to focus on more complex tasks requiring higher-level thinking.
  • Even if the scope of these tasks and the intelligence required can vary from job to job, as AI has pushed back mechanical labour, humans will need to focus on tasks that AI is unlikely to take on, namely those that require “thinking” and “feeling” skills (Huang & Rust, 2018; Huang et al., 2019).
    REFLECTION: Especially in medical diagnosis,I can see how AI could help a service provider synthesize the massive amount of information available that is impossible to keep in one’s mind at all times, especially staying up to date on new research. AI can serve as a cognitive assistant while the human person can focus on the feeling skills at work—compassion, care, warmth, connection.
  • AI can help organizations automate some processes, but employees still need to use their creativity to come up with new ideas, think outside the box, and solve problems that AI systems cannot. AI systems can save time by automating or speeding up the time-consuming or repetitive low-level tasks, leaving employees to use their brainpower to focus on tasks requiring creativity, innovation, empathy, or other qualities that are unique to humans.
    REFLECTION: I hadn’t thought through some of the potential benefits of this, though I do think that sometimes it’s good to have those mindless tasks to prime the mind for bigger, more creative endeavors. The human brain is not typically able to be creative 100% of the time. The balance of mindless tasks fills the gap and helps make the “non creative” time continue to be productive.

Touretzky, D., Gardner-McCune, C., Martin, F., & Seehorn, D. (2019, July). Envisioning AI for K-12: What should every child know about AI?. In Proceedings of the AAAI conference on artificial intelligence (Vol. 33, No. 01, pp. 9795-9799).

SUMMARY: This article presents the findings of a joint task for on AI in education as presented at The Thirty-Third AAAI Conference on Artificial Intelligence in 2018. According to and implied in the report: Because AI is the newest disruptive technology that is shaping the future (and present) of AI (what many call “the fourth industrial revolution”), it is prudent for countries concerned with maintaining economic dominance to follow China’s lead and ensure that its citizens are educated about AI, both as users and potentially as developers. This document outlines 2018 US national guidelines for teaching AI to K-12 students made by a joint working group of the Association for the Advancement of Artificial Intelligence (AAAI) and the Computer Science Teachers Association (CSTA).

  • Understanding people is one of the hardest problems faced by intelligent agents. This includes tasks such as conversing in natural language, recognizing emotional states, and inferring intentions from observed behavior. Students should understand that while computers can understand natural language to a limited extent, at present they lack the general reasoning and conversational capabilities of even a child. In grades 3-5 students should be able to distinguish a chatbot from a human, and analyze natural language examples to determine which ones would be difficult for a computer to understand, and why.
    REFLECTION: Adults should too!
  • “In grades 3-5 students should be able to distinguish a chatbot from a human, and analyze natural language examples to determine which ones would be difficult for a computer to understand, and why. In grades 6-8 students should be able to use parser demos to demonstrate syntactic parsing of sentences, and construct sentences that purely syntactic parsers will mishandle due to problems such as erroneous prepositional phrase attachment (e.g., ‘I pour syrup for pancakes from a bottle’).”
    INTERESTING: Human knowledge of grammar will still matter!

Deruty, E., Grachten, M., Lattner, S., Nistal, J., and Aouameur, C. (2022). On the Development and Practice of AI Technology for Contemporary Popular Music Production. Transactions of the International Society for Music Information Retrieval, 5(1), 35–49. DOI: https://doi. org/10.5334/tismir.100

SUMMARY: A team from Sony Computer Science Laboratories (CSL) in Paris provided recently developed Sony AI tools and prototypes to six artists. The artists applied the tools to music creation, and then shared feedback on what they liked and did not like, alongside observations on how it affected their creative process. The paper presented recommendations for the use of AI in contemporary popular music based on artist feedback.

  • AI-generated ideas helped “break creative habits” and allowed the artist to “reflect on one’s own creative practice and aesthetic values.” This resonates with thinking on the nature of creativity, which has moved beyond seeing creativity as a function of individuals but rather stemming from interactions between individuals in a social environment according to conventions and attributes of a specific domain. This idea has important implications for teaching creative endeavors in an online setting, and points to the critical importance of finding ways to increase student-to-student interaction in creative projects through group work or other forms of cooperation and idea sharing.
    REFLECTION: What role can AI play in increasing student interaction… or… gasp… serving as a proxy for student interaction?
  • Many issues may surround the use of authorship and creative ownership when using AI in-studio music creation. AI further complicates an issue that is already in play. “Composition” and “ownership” today is more complex than the traditional more linear view, which involves a single composer or artist(s) realizing their vision with the help of producers and engineers. Now, music is created in-studio and the final production emerges as a team effort of the musicians as well as the multiple individuals engaged in the production and editing process.
    REFLECTION: What happens when we add AI to the composition mix? Artists in this article expressed interest in having AI as a tool that they would incorporate into their creative process—but not replace it. Who benefits from the creation of an AI system that operates autonomously—creating music independently of any inputs from human artists?

On the Development and Practice of AI Technology for Contemporary Popular Music Production

[Photo: Berklee Online.]

ANNOTATION: Deruty, E., Grachten, M., Lattner, S., Nistal, J., and Aouameur, C. (2022). On the Development and Practice of AI Technology for Contemporary Popular Music Production. Transactions of the International Society for Music Information Retrieval, 5(1), 35–49. DOI: https://doi. org/10.5334/tismir.100

The music industry has been abuzz with discussion on AI’s entry into contemporary popular music creation. It is used in a number of ways, including: to create new lyrics, to synthesize individual sounds, to break recorded tracks into their individual parts (referred to as “stems”), and to create complete musical tracks. The question is, which parts of music creation will AI be most useful and relevant for, and how can it best be included in a musician’s creative workflow?

To answer this question, a team from Sony Computer Science Laboratories (CSL) in Paris provided recently developed Sony AI tools and prototypes to six artists. These tools included either standalone applications, VST plug-ins for digital audio workstations (DAW), or servers accessible through a web-interface. These tools offer AI contributions to a variety of aspects of the music production process, from sound design to mixing, equalization, and the generation of melodic and rhythmic material. The tools are primarily pull interactions, where the user explicitly queries the tool for an output using priming—for example, serving as the start of a musical part to be continued by the tool, as the starting template from which variations can be explored, or as a part in a multi-part setting for which the tool generates accompanying parts.

Though there are several contexts in which AI tools might be used to create music, this paper focuses on in-studio music creation, which today is more complex than the traditional more linear view, which involves a single composer or artist(s) realizing their vision with the help of producers and engineers. Now, music is created in studio and the final production emerges as a team effort of the musicians as well as the multiple individuals engaged in the production and editing process.

The six artists readily participated in this study not as “subjects” but as an opportunity to use the tools in music creation and then provide feedback. The artists were offered the tools and then interviewed about what they liked and did not like, and provided observations on how it affected their creative process. Artists answers were categories and subjected to a thematic analysis, all guided by the overarching question: how do artists use the AI tools?

One standout comment was that the AI-generated ideas helped “break creative habits” and allowed the artist to “reflect on one’s own creative practice and aesthetic values.” This resonates with thinking on the nature of creativity, which has moved beyond seeing creativity as a function of individuals but rather stemming from interactions between individuals in a social environment according to conventions and attributes of a specific domain.

The researchers identified the following four primary lessons learned from this study:

  • AI in music creation should work alongside artists. The artists in this case study were happy to use this as a tool to create ideas, but not a replacement for their own creativity.
  • A good tool affords the possibility of unexpected results. For example, the “glitchy” and “imperfect” parts of music created by AI were interesting to artists because they created unexpected effects that contributed to interesting ideas in the final musical product.
  • AI can be a part, but not all of a production. Artists still want to be part of the process.
  • Adapt to the music at hand—meaning, create products that are familiar to the workflow that artists are already used to in contemporary music making.

To determine the validity of AI in music production—in other words, to develop a product that musicians will actually like and use—artists’ feedback suggested the following: Any effective AI tool needs to integrate smoothly into existing workflows, make complicated tasks easier not harder, enhance creativity and provide ideas but not replace them, have clearly identifiable results, and be publishable. If these six artists were representative of the larger population, it is clear that AI may have a role in helping to make complicated tasks more simple, and provide creative fodder and help artists break through creative blocks, but if any AI product is to be adopted widely, its developers need to ensure that the artist feels that they are part of the creative equation.

Using Peer Feedback to Enhance the Quality of Student Online Postings: An Exploratory Study

ANNOTATION: Ertmer et al. (2007). Using Peer Feedback to Enhance the Quality of Student Online Postings: An Exploratory Study. Journal of Computer-Mediated Communication 12: 412–433.

Ertmer et al’s study is a case study of 15 students to assess whether peer feedback improves the quality of online postings in an online course. Using numerical, rubric-driven grades, participant interviews, and entry and exit questionnaires, researchers considered students’ assessments of the educational value of giving and receiving peer feedback. Their specific goal was to determine whether the peer feedback resulted in an improvement in the quality of discussion posts according to Bloom’s taxonomy. Students numerically ranked the quality of peer posts and provided some text-based feedback. Researchers compared the quality of student posts from early and late in the course to determine whether quality improved, where quality is defined as comments that reflect higher-order thinking. Though students stated no change in their preference for instructor feedback over peer feedback, they did report that both giving and receiving peer feedback was helpful to their learning.

Regarding the paper quality: Through a well-structured theoretical framework, the authors clearly state much research that supports the value of feedback in the learning process. They define what constitutes helpful feedback, noting that feedback is a frequently cited catalyst for learning and that lack of feedback is a primary reason for withdrawal from online courses. (Interestingly, they provided no citations to support those particular assertions.) The researchers indicated that responding to discussion posts can be labor-intensive for faculty, and so peer feedback may help relieve some of the pressure. However, the way that they structured the research seemed to make the process even more intensive for faculty. Subjects were trained in the use of a Bloom’s-based rubric to evaluate their peers, but peer evaluations went through a faculty-vetting process before feedback was returned. This created a two-week delay in getting feedback and thus came too late to be incorporated into subsequent work. The research process seemed labor intensive in general, so it made sense that they used a case study approach with an appropriately-sized sample of 15. However, because of this delay the research was challenged by test-retest reliability. A similar research project delivered over the course of several semesters with a more widely representative sample of students may yield more reliable results.

A few key questions are left unanswered after my first read of this work, and I’m sure more will emerge following the upcoming Critical Review of Research assignment on this article. First, is a case study format appropriate for this kind of research? It is certainly convenient, as the research process seemed labor intensive, but is it reliable? Second, how representative is this sample group, and can results be reliably applied in other contexts? The sample group consisted of mostly graduate students in educational research, including educational administrators who presumably are already highly trained in providing effective feedback. To me, this was in no way an unbiased representative sample. Third, what were the discussion questions that were asked? The quality of a question helps to determine the quality of the answer (Meyer, 2004), and though the researchers provided one good example of a discussion question that would seem to yield “higher order” thinking and synthesis, it’s hard to know whether all of the questions were of equal quality.

Tangentially related to this, I learned two things through the lit review that are worthy of further reading: 1) Online discussion forums need to be carefully considered, as they typically require students to communicate complex ideas through written text, rather than a conversation, and this can be a barrier for some. 2) Some students feel anxiety around giving feedback to peers.

What tools or technologies are used to improve engagement?

FOCUSED LITERATURE REVIEW 8: Exploring the potential of LMS log data as a proxy measure of student engagement

SOURCE: Henrie, C. R., Bodily, R., Larsen, R., & Graham, C. R. (2018). Exploring the potential of LMS log data as a proxy measure of student engagement. Journal of Computing in Higher Education, 30(2), 344-362. https://doi.org/10.1007/s12528-017-9161-1

Engagement—defined in this study as focused, committed energetic involvement in learning—has been shown to be directly correlated with academic success, and is particularly crucial in technology-mediated distance learning. This study looked at the efficacy of a learning management system as a tool for engagement. The researchers specifically sought a correlation between self-reported engagement survey scores and log data tracking student activity in the Canvas learning management system.

While many kinds of engagement are identified in the literature, the researchers focused on cognitive and emotional engagement because these types in particular have a strong empirically and theoretically supported connection to learning. The researchers found that LMS log data, which would seemingly be a strong indicator of learning activity in a course, did not have a statistically significant relationship to students’ self-reported measures of cognitive and emotional engagement in online courses.

The researchers looked at data from 153 students in 8 individual sections of three undergraduate courses at a single university in the western US. All courses were offered through Canvas in a blended format. Evaluating log data was chosen as a measure of engagement because it was minimally disruptive to each learner’s process since it is automatically tracked behind the scenes. They measured: URLs visited, page views, time spent per page, time stamps of page visitation, number of discussion replies, punctuality of assignment submission, and grades. Each course included discussion boards, quizzes, online videos, and projects, all of which took place within the LMS.

This data was compared against the survey, which consisted of 7 Likert-style questions. Self-report was chosen as a measure of student engagement because cognitive and emotional states are hard to measure by observation and conclusions drawn via observation are highly inferential.

Their unexpected results, they concluded, point to the complexity of learning and the difference between observed measures of engagement versus students’ self-reported intellectual and emotional states. The researchers noted a long list of limitations to their research that may have yielded their unexpected results, and they concluded that behavior captured through log data may be far more complicated than they realized. To yield more reliable results, they suggest that other factors need to be accounted for to better understand what it means to be engaged in online learning, such as previous knowledge, motivation to learn, or level of confusion or frustration.

They suggest further work with other methods for measuring student engagement like mouse tracking, physiological instruments, and human observers. A more complex and longer survey may also help gather additional nuance related to time spent on pages and on assignments. For example, high engagement and motivation may actually require less time of the students, while frustration and confusion may mean more time spent on pages. Also, time spent on pages is difficult to quantify, as without direct observation, it is difficult to determine whether a student was actually focused on their work while a page happens to be open on screen.

What are strategies that enhance engagement in online courses?

FOCUSED LITERATURE REVIEW #7: “Engagement matters: Student perceptions on the importance of engagement strategies in the online learning environment“

Martin, F. & Bolliger, D.U. (2018). Engagement matters: Student perceptions on the importance of engagement strategies in the online learning environment. Online Learning 22(1), 205- 222. doi:10.24059/olj.v22i1.1092

In their research study, Martin and Bolliger issued a 38-question survey to 155 students in a variety of programs at 8 geographically and structurally diverse U.S. universities, asking online learners questions about three key engagement strategies in recent courses: learner-to-learner, learner-to-faculty, and learner-to-content. The researchers sought to answer three questions:

  1. Which strategies do students perceive to be most important in the three categories of engagement?
  2. Which strategies do learners identify as most valuable and least valuable to engaging them in the online environment?
  3. Are there differences in responses based on age, gender, and years of online learning experience?  

These questions led to the following findings:

  • In the learner-to-learner category, in which students feel a dynamic sense of community, students rated introduction discussions, icebreakers, and collaborative projects using online tools as most beneficial. Least beneficial was “virtual lounges” for informal discussions outside of structured class activities.
  • In the learner-to-instructor category, students noted instructors sending regular announcements, email reminders, timely feedback, and the provision of grading rubrics for all assignments as most beneficial. Reflection activities were rated least favored for engagement—though this is inconsistent with much prior research on the topic.
  • In learner-to-content—which refers to the intellectual interaction with content, including reading online, watching videos, taking online quizzes, and completing assignments—students preferred real-world projects, case studies, and discussions with structured or guiding questions as most beneficial to their engagement. Many students rated synchronous meetings as least beneficial, though this contradicts much prior research on the topic.

Definitions of engagement vary widely in the literature (Halverson and Graham, 2019), and the terms “interaction” and “engagement” are often used interchangeably. Accordingly the authors chose Moore’s framework (1993) on the three kinds of interaction to measure engagement. In the presentation of their framework, they cite subsequent research specifically in online learning that supports the validity and value of each of the three kinds of engagement. As such, implementing strategies to increase engagement is critical to improving student learning and student satisfaction in online courses, even moreso in an age where engagement has dethroned content as king (Banna et. al, 2015). Citing a wide body of research, Martin and Bolliger conclude that “Interactivity and sense of community result in high-quality instruction and more effective learning outcomes.”

Participation in this study was voluntary. The research sample was 67% female, and the survey respondents were not surprisingly primarily involved in the study of education. More than half of them were graduate students, and all were adults.

Notably, among adult learners, the simultaneously most and least favored activity was online discussion forums. One student commented that online forums felt like busy work, even when well-designed. Two other common strategies, synchronous meetings and videos, were rated as most valuable by some and least valuable by others. All of this clearly depends on the quality of the video and applicability to the work, and regarding sync meetings, this may be related to students’ reasons for taking online courses in the first place. Notably, those trying to manage an already busy schedule found it counterproductive to have to schedule and attend synchronous meetings. Additional strategies rated very important were: a variety of instructional materials (video, readings, web resources, multimedia), structured discussions, and real-world application.

Regarding age, gender, and experience, the only statistically significant results were the following: females appreciated having access to additional resources to explore in more depth; younger students appreciated more frequent check-ins from the instructor, and students with less online learning experience appreciated having more opportunities for online “hangouts,” check-ins from the instructor, and greater variety of content.

Interestingly, the most appreciated activity across the board was ice-breaker activities, followed by collaborative activities using online tools. The least important was the idea of a virtual lounge for informal discussions outside of class—but note that this study was of mostly adult students who likely have busy lives outside of school. Secondly, it is important to note most of the courses reported on in this study were entirely asynchronous.

It is important to note the study’s limitations: a relatively small sample size, the fact that data was self-reported, and the somewhat limited list of strategies included in the survey. Also, the researchers had no control over the design of the courses, the delivery of them, or the instructors. Despite these limitations, this 2019 study has been cited in 1462 other works as of 11/8/2023.

The most important aspect of this study confirms what much other research has also shown: Instructor presence is everything. In short: students “want to know that someone ‘on the other end’ is paying attention. Online learners want instructors who support, listen to, and communicate with them.”

SOURCES NOTED:

Banna, J., Lin, M.-F. G., Stewart, M., & Fialkowski, M. K. (2015). Interaction matters: Strategies to promote engaged learning in an online introductory nutrition course. Journal of Online Learning and Teaching, 11(2), 249–261.

Halverson, L.R., & Graham, C.R. (2019). Learner engagement in blended learning environments: A conceptual framework. Online Learning, 23(2), 145-178. doi:10.24059/olj.v23i2.1481

Moore, M. J. (1993). Three types of interaction. In K. Harry, M. John, & D. Keegan (Eds.), Distance education theory (pp. 19–24). New York: Routledge.

The New Literacy Studies and the Resurgent Literacy Myth

ANNOTATION: Graff, H.J. (2022). The New Literacy Studies and the Resurgent Literacy Myth. In: Searching for Literacy: The Social and Intellectual Origins of Literacy Studies. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-96981-3_9

Graff’s chapter in Palgrave Macmillan’s 2022 release Searching for Literacy is a blazing takedown of New Literacy Studies, claiming that much of the research in this area never defines what it means by the term, lacks the evidence of empirical or theoretical studies, and totally disregards the significant research and seminal works in the field, ignoring the rich history of thinking that had its roots in the educational reform movement of the 1960s. Worse, in many cases, the concept of “multiple” literacies today is loosely and baselessly thrown around by corporate interests as a way to sell educational and other types of products.

Graff’s “literacy myth” takes aim at the “unique and innate power of ‘literacy by itself.’” His position is that new writers in the “new literacy” rarely define what they mean by the term “literacy.” It is, he writes, a problematic term in that there is no freestanding entity called “literacy,” and that “literacy”—and indeed education aimed at producing it—can never be free of context. Literacy is inextricably tied to a value system and to the complex web of conditions associated with a sense of advancement, superiority, and progress or “success,” all of which are culturally and/or socially determined by those with the power to define them. The “myth” he refers to is the belief that “the acquisition of literacy is a precursor to and inevitably results in economic development, democratic practice, cognitive enhancement, and upward social mobility.” The use of the term “myth” is not to suggest that literacy does not lead to advancement (it can, in many but not all cases). The “myth” refers to the concept of literacy itself as autonomous. Rather, it is contextual and ideological.

Graff’s definition of literacy rests on the foundations of reading, writing, and sometimes arithmetic. In contrast, the new literacies refer to skills in many and multiple domains. He lists 36 types of literacies found in a simple online search—from reading and writing to data, multimodal, media, civic and ethical, financial, health and medical, and many more. It is problematic, he writes, that the different literacies are rarely compared, interrelated, or evaluated. A sense of chaos results, blurring the lines between “scholarship and education on the one hand, and promotion and sales, on the other.”

The reader is left to ponder the difference between lower-case literacy (a general term used to describe the possession of a specific set of knowledge and skills) and uppercase Literacy, Graff’s “foundational reading, writing, and in some cases arithmetic” definition. Graff’s is a tightly written chapter that makes a great deal of sense, though the reader (at least this reader) is also left to wonder how much of his position is flavored by sour grapes: His own book, The Literacy Myth (1979), was never cited in the 2020 Routledge Handbook of Literacy Studies.

He likes this new Palgrave MacMillan book so much better.

What is engagement and is it different than motivation?

FOCUSED LITERATURE REVIEW #6: “The Relationship Between Student Motivation and Class Engagement Levels”

Nayır, F. (2017). The Relationship between Student Motivation and Class Engagement Levels. Eurasian Journal of Educational Research, 17 (71), 59-78.

This post is a little different than previous. EDU 811 “Motivation in Online and Blended Learning” requires weekly-ish deep reads of one article, accompanied by a 300-word annotation. The assignment is somewhat similar to EDU800’s weekly annotations, but these are focused on answering a teacher-led prompt. (Naturally, mine are regularly 600+ words… “I would have written a shorter letter but I didn’t have enough time.”) Because my writing in these have been of varied “just get it done on time” quality and posting is not required, I haven’t posted them, but I’m going to start revisiting them and will be posting as I get around to revising each.

This week’s article was particularly inspiring to me in that it made clear connections among a variety of theoretical frameworks and clarified many terms commonly used in studies of motivation. So, forgive me, but this entry is kind of list-heavy. For a reason: It helped me organize my thoughts.

Motivation, engagement, and learning are discrete concepts, yet in an educational setting they are mutually interdependent. Motivation is the driving force that spurs students to act; engagement is the observable, behavioral evidence of that motivation,[1] and learning is directly correlated with engagement. The equation is straightforward: To increase learning, increase engagement, and to increase engagement, increase motivation. But how—especially with teens?

An abundance of research indicates that as students advance to higher grades, they become less engaged in school. A 2018 Gallup poll discovered that by high school the number of “engaged” students shrinks to 33% from 74% in fifth grade (Parrish, 2017), and the research presented in this focused literature review, “The Relationship between Student Motivation and Class Engagement Levels” confirms this (Nayir, 2017). Nayir’s findings suggest that it is particularly important for high school educators to focus on motivational factors in order to engage students and thus improve learning outcomes.

Nayir’s study involved 500 students in a random sample from public high schools across Ankara, the capital of Turkey. The study set out to determine the relationship between student engagement and motivation using the theoretical framework of Self-Determination Theory (Desi & Ryan, 2000) and student engagement levels as defined by Schlecty (2001). Through a relational study using the Pattern Adaptive Learning Scale developed by Midgeley et al (2000), the study found that mastery-oriented learning (intrinsic motivation focused on mastering a topic) predicts all levels of classroom engagement, that vocational students are affected more by motivational factors, and that motivation level declines as grade level rises.

Most interesting to me in this study was the author’s theoretical framework setup, which served as a very helpful overview of the connections between motivation and engagement. The author cites research that shows that the more students are engaged in academic activities, the more successful they are.

Engagement has been categorized into three dimensions: emotional, behavioral, and cognitive (Fredricks, Blumenfeld, and Paris, 2004).

  • Emotional. Positive and negative reactions to classmates, student attitude, perception of the value of learning, interest and enjoyment, happiness, sense of belonging at school.
  • Behavioral. Participation, presence, compliance with rules, effort, persistence, concentration, involvement.
  • Cognitive. Learning by choice, investment, self-regulation, goal setting, thoughtfulness, mastery orientation, resiliency, persistence, self-efficacy.

Within each of these categories, the student assigns a value to this engagement, which also can be hierarchically stratified (Schlecty, 2001):

  • Authentic engagement: Students find personal meaning in activities.
  • Ritual engagement: Students do what is required.
  • Passive compliance: Students expend minimum effort to avoid punishment.
  • Retreatism: Students reject learning activities and emotionally disengage.
  • Rebellion: Students reject class activities and substitute them with their own objectives, which may be disruptive.

According to Deci & Ryan (2009), the founders of the widely-cited Self-Determination Theory (SDT), motivation is a prerequisite of student engagement in the learning process. According to SDT, motivation emanates from three universal dimensions of human need:  competence (the desire to be good at something or adaptive to the environment), autonomy (choice in the matter and self-direction), and relatedness (a feeling of connection and belonging). Motivation can be measured along a hierarchical continuum from amotivation to extrinsic to intrinsic.

  • Amotivation: No value is attributed to actions.
  • Extrinsic: External influences or reward-driven actions.
  • Intrinsic: Enjoyment or interest-driven actions.

These levels of motivation correlate with the five levels of engagement:

  • Intrinsically motivated students show the highest level of engagement, authentic engagement.
  • Extrinsic motivation manifests as ritual engagement, passive compliance, and retreatism.
  • Amotivation typically leads to rebellion.

What these frameworks reveal is, again, that the best way to increase learning is to seek ways to increase intrinsic motivation. Ryan and Deci (2002)’s work suggests that attributing meaning to learning is Job 1 for motivation, and thus engagement, and thus learning. They further suggest that goal orientation is critical toward this end, confirmed in research from Midgeley et al (2000), who studied goal orientation and described three kinds:

  • Mastery goal orientation. Individuals have self-efficacy, are aware of their strengths, and believe in their ability to succeed… and they want to.
  • Personal performance-approach goal orientation. These individuals compare themselves to others and are motivated by competition.
  • Personal performance-avoidance goal orientation. Individuals try to hide their failures, fear mistakes, and expect very little success.

Not surprisingly, much research supports a strong connection between intrinsic motivation and mastery goal orientation. Nayir’s study and the research it cites show that offering mechanisms to spur intrinsic motivation is job #1 to improve learning, especially with high school students.

Citations:

The article cites many excellent resources on the topics of motivation and engagement. The list below indicates the most significant among them to this particular focused literature review.

Fredricks. A., Blumenfeld P.C., & Paris A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74, 59-109.

Midgley, C., Maehr, M., Hruda, L., Anderman, E., Anderman, L., & Freeman, K., et al. (2000). Manual for the patterns of adaptive learning scales. Ann Arbor, MI: University of Michigan.

Parrish, N. (2017, November 2022.  To Increase Student Engagement, Focus on Motivation. Edutopia. https://www.edutopia.org/article/to-increase-student-engagement-focus-on-motivation

Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25, 54-67. http://dx.doi.org/10.1006/ceps.1999.1020

Ryan, R.M & Deci, E.L. (2009). Promoting self-determined school engagement: Motivation, learning and well-being, In Wentzel, K.R & Wigfield, A. (Eds), Handbook on motivation at school. New York: Routledge, 171-196.

Schlechty, P. C. (2001) Okulu yeniden kurmak, (Çev. Özden, Y., 2012) Ankara: Nobel Yayıncılık.

Schlechty P.C. (2002). Working on the work. San Francisco, CA: Jossey-Bass.

[1]Schlecty’s research would indicate otherwise.

Self-determination theory: An approach to motivation in music education

ANNOTATION: Evans, Paul (2015). Self-determination theory: An approach to motivation in music education. Musicae Scientiae, 19(1), 65-83. doi: 10.1177/10298649/4568044.

Though numerous motivational models have been previously applied to understand motivation in music learning, no theoretical framework has been universally accepted. In this article, Evans provides an argument for Self-Determination Theory as an ideal theoretical model for understanding and describing why students take up an instrument, how they persist through the many challenges encountered over the long time required to acquire competence, and how they either achieve success or quit.

Deci and Ryan’s widely cited self-determination theory (SDT) suggests that motivation arises from a tendency towards personal growth and a unified sense of self (Maslow’s “self-actualization” comes to mind), supported by three universal psychological needs: competence, autonomy, and relatedness. Evans provides an SDT-based conceptual overview of motivation in music learning by presenting a wide variety of research projects that have applied it successfully to issues in music education.

His conclusion is that many of the typical ways that teachers and parents have encouraged their students and children to practice are misguided, in that they use external reward/punishment, coercion, excessive praise, and competition, all of which have been shown repeatedly in research to be demotivating over the long term. The best solution, he writes, is for parents and teachers to create social environments in music where students are more apt to generate their own interest and enjoyment by identifying the value of musical practice, aligning it with their sense of self, and finding intrinsic motivation in music making for the enjoyment of the activity in and of itself.

This article is worthy of a deeper read and would serve as a helpful launch point for further study, as its theoretical framework and literature review point to a rich treasure trove of research on motivation in instrumental education. The most interesting ideas I culled from this confirmed my own observations as a teacher:

  1. Achievement “star charts” are demotivating for many students, and for the highest achieving “star gatherers,” the motivation to continue achieving declines rapidly the moment the final star is achieved. I witnessed over and over that especially young boys were eager to reach the final star at the end of the chart, and then their interest dropped almost immediately once they finished the “final star.” Likewise, students who did not achieve as quickly typically dropped out, not because they were not interested in music but because their relative slower pace on the star chart made them question their self-efficacy.
  2. Kindness matters in early music education. Under the SDT motivator of “relatedness,” Bloom (1985) found three stages to teacher relationships, and that in the early years, students enjoy and thus persist with teachers whose lessons are fun, informal, and enjoyable; slightly more demanding in middle years; and holding much higher standards in later years where the teacher and student engage together in a pursuit of mastery.
  3. Students who could choose their repertoire typically showed higher motivation.

Evans’ application of self-determination theory to music learning has some implications for teaching an instrument in an online context. Because motivation is so central to instrument learning in general, then online instrumental teachers might consider the following: 1) pay triple attention to activities that can build relationships to support the “relatedness” factor, 2) provide support and carefully scaffolded lessons (not just effusive praise) to continually build students’ confidence through their growing sense of competence, 3) provide choice, self-direction, and autonomy in creative projects.

Understanding feedback in online learning—A critical review and metaphor analysis

ANNOTATION: Jenson, Lasse X., Bearman, M. and Boud, D. (2021). Understanding Feedback in Online Learning: A Critical Review and Metaphor Analysis. Computers & Education 173 (4), 104271. doi.org/10.1016/j.compedu.2021.104271

Jenson et al.’s paper is a critical review of online learning research to explore the way that researchers define and conceive of the concept of “feedback” in e-learning. The researchers completed a qualitative analysis of the language used to describe feedback in four leading research journals and identified six discrete meanings or “understandings” of the term based on what they refer to as conceptual metaphors. Metaphors are helpful in understanding complex concepts in that they provide a simpler, more concrete representation of a term that is by nature abstract and complex. However, in simplifying a complex concept, metaphors create conceptual entailments—that is, they limit a researcher’s view of a concept because they limit how a thing is seen based on the metaphor being used to approximate it.

To complete their work, they analyzed 17 articles published between January 2017-February 2019 in four of the leading journals in e-learning, including Computers & Education, British Journal of Educational Technology, Journal of Computer Assisted Learning, and The Internet and Higher Education. They identified six dominant metaphors to help organize the great disparity that exists in the numerous applications of the term, “feedback.”

The six metaphors for feedback that they identify are:

  1. Feedback is a treatment. (11/17 papers) Feedback serves as an intervention and learning improvement is an effect caused by feedback.
  2. Feedback is a costly commodity. (5/17 papers). Feedback is positioned as time-consuming and burdensome on faculty.
  3. Feedback is coaching. (7/17) The main purpose of feedback is to motivate learners.
  4. Feedback is a command. (5/17 papers) Feedback is controlling and directive.
  5. Feedback is a learner tool. (7/17) Here, agency lies with the learner to take the feedback and apply it to further learning.
  6. Feedback is a dialogue. (6/17 papers). The most in line with contemporary thinking on the value of feedback, here feedback is a productive discussion between the learner and peers or the instructor, and the learner then applies the feedback to improve performance.

The findings of this study indicate that only the last two of the six feedback metaphors that are used in the research relate specifically to known best practices for learner-centric practice. The first four of the metaphors reflect feedback practices that are considered inappropriate among researchers because they place the instructor as the main agent in the feedback process and assume that this feedback automatically leads to learning.

Overall, they found little agreement among instructors and students about what the purpose of feedback is. They conclude that if researchers are attempting to improve practice for educators and learners, they need to be clear about their definition of feedback in order to be specific about the effect of that feedback and about any improvements that need to be made as a result. Researchers also need to focus their work on the kinds of feedback that are widely considered to be good feedback practices.

The Effect of Individualized Online Instruction on TPACK Skills and Achievement in Piano Lessons

ANNOTATION: Kaleli, Y. S. (2021). The Effect of Individualized Online Instruction on TPACK Skills and Achievement in Piano Lessons. International Journal of Technology in Education 4(3), 399-412. doi.org/10.46328/ijte.143

This study worked with a control group and experimental group of music education majors to compare student learning outcomes and performance in online piano instruction via a pre- and post-test after 20 hours over 10 weeks of piano instruction.

The work was conducted during the pandemic, a response to the rapid transition to online learning that occurred during the pandemic. According to the author, that transition brought to light just how little some universities know about effective online learning, especially ion arts courses. Further, the author wrote, as the pandemic wound down, it became clear that online learning is here to stay and there is still not enough understanding of the best ways to teach online. The author also pointed to a number of studies that provide that “the positive contribution of technology to education is undeniable.”

This particular study of piano learning was conducted with 30 music education majors at a four-year university in Turkey around 2020. Piano skills are required of all music education majors, and the author suggested that a particular challenge with music learning is that traditionally, teachers teach private lessons via a highly individualized style that suits the learning needs, level, and pace of each individual student. Thus, any system to teach music should be designed with individuation in mind, but current online learning models are not designed as such.

The experimental group received one hour of online education and one hour of face-to-face instruction, and the distance learning group did not have face-to-face instruction. The material covered was the same in both groups, reflecting the first-level national standards for piano skills: knowledge of C major and A minor, four octaves, major and minor sounds, a few simple cadences, and staccato and legato articulation.  Overall, the study showed that the experimental group, who combined some online learning with face-to-face learning, performed far better on a Piano Lesson Achievement Test post-test. The researchers also used a TPACK model of self-reported measures of self-efficacy and technological skills, determined via 47 items on a 5-point Likert scale post-test. The study defined the TPACK (Technology Pedagogy and Content Knowledge) model as “dynamic, procedural integration knowledge between technology, pedagogy and content, and how this interaction affects student learning in the classroom.” [sic. The article’s translation was not well-edited in English.] As such, the study also sought to determine whether technological training in combination with pedagogical and content knowledge would yield better student outcomes.

There are a few issues that limit the reliability of the study results. For one, the paper does not clearly define the difference between online education and distance education, so one can only assume from the various mentions of it that “distance education” meant teaching entirely via Zoom, as the author referred to online video conferencing apps as the means by which distance education is conducted. Second, the paper does not include any description of the instructional design of the online portion of the course, but since the same design was used for both the control and experimental groups, it may not be all that significant that this was not mentioned. Though there is a lack of clarity on many terms, the study does support the conclusion that a combination of asynchronous and synchronous learning produced better results than entirely asynchronous online learning—a finding that has been reported in a wide variety of previous studies in other fields, as noted by the author.