Category: pedagogical agents
Last week, a reporter from EdSurge reached out to me to shed some light on what Pearson called their Learning Design Principles. The EdSurge article is here, but below is a more detailed rough draft of the points that I made to share. I am posting them here for a fuller picture of some of my thoughts.
- Nothing proprietary (yet, perhaps). I saw a number of sources note that Pearson released their proprietary learning design principles. There’s not much proprietary in the principles. All of these ideas are well-documented in the literature pertaining to educational technology found in cognitive psychology, learning sciences, instructional design, and education literature.
- It’s good to see that Pearson is using findings from the education literature to guide its design and development. Some of these principles should be standard practice. If you are creating educational technology products without considering concepts like instructional alignment, feedback, and scaffolding, authentic learning, student-centered learning environments, and inquiry-based learning, you are likely creating more educational harm than good. The point is that using research to guide educational technology should be applauded and emulated. More educational technology companies should be using research to inform their designs and product iterations.
- BUT, since around 2011, the educational technology industry has promoted the narrative that education has not changed since the dawn of time. With a few exceptions, the industry has ignored the history, theory, and research of the academic fields associated with improving education with technology. The industry has ignored this at its own peril because we have a decent – not perfect, but decent – understanding of how people learn and how we can help improve the ways that people learn. But, the industry has developed products and services starting from scratch, making the same mistakes that other have done in the past, while claiming that their products and services will disrupt education.
- Not all of the items released are principles. For example, “pedagogical agents” is on the list but that’s not a principle. Having studied the implementation of pedagogical agents for more than 7 years, it’s clear that what Pearson is attempting to do is figure our how to better design pedagogical agents for learning. Forgive me while I link to some pdfs of my past work here, but, should amagent’s representation match the content area that they are supporting (should a doctor look like a doctor or should she have a blue mohawk?). Table 1 in this paper provides more on principles for designing pedagogical agents (e.g., agents should establish their role so that learners have a clear anticipation of what the agent can and cannot do: Does the agent purport to know everything or is the agent intended to ask questions but provide no answers?)
- As you can tell from the above, I firmly believe that industry needs research/researchers in developing, evaluating, and refining innovations.
But more importantly, happy, merry, just, and peaceful holidays to everyone!
I will be visiting my colleagues at the University of Edinburgh in mid-June to give a seminar on MOOCs, automation, artificial intelligence and pedagogical agents. This is a free event organized by the Moray House School of Education at the U of Edinburgh and supported by the Digital Cultures and Education research group and DigitalHSS. Please feel free to join us face-to-face or online (Date: 18 June 2014; Time: 1-3pm) by registering here.
This seminar will bring together some of my current and past research. A lot of my work in the past examined learners’ experiences with conversational and (semi)intelligent agents. In that research, we discovered that the experience of interacting with intelligent technologies was engrossing (pdf). Yet, learners often verbally abused the pedagogical agents (pdf). We also discovered that appearance (pdf) may be a significant mediating factor in learning. Importanly, this research indicated that “learners both humanized the agents and expected them to abide by social norms, but also identified the agents as programmed tools, resisting and rejecting their lifelike behaviors.”
A lot of my current work examines experiences with open online courses and online social networks, but what exactly does pedagogical agents and MOOCs have to do with each other? Ideas associated with Artificial Intelligence are present in both the emergence of xMOOCs (EdX, Udacity, and Coursera emanated from AI labs) and certain practices associated with them – e.g., see Balfour (2013) on automated essay scoring. Audrey Watters highlighted these issues in the past. While I haven’t yet seen discussions on the integration of lifelike characters and pedagogical agents in MOOCs, the use of lifelike robots for education and the role of the faculty member in MOOCs are areas of debate and investigation in both the popular press and the scholarly literature. The quest to automate instruction has a long history, and lives within the sociocultural context of particular time periods. For example, the Second World War found US soldiers and cilvilians unprepared for the war effort, and audiovisual devices were extensively used to efficiently train individuals at a massive scale. Nowadays, similar efforts at achieving scale and efficiencies reflect problems, issues, and cultural beliefs of our time.
I’m working on my presentation, but if you have any questions or thoughts to share, I’d love to hear them!
Social and non-task interactions are often recognized as a valuable part of the learning experience. Talk over football, community events, or local news for example, may enable the development of positive instructor-learner relationships and a relaxed learning atmosphere. Non-task aspects of learning however have received limited attention in the education literature. Morgan-Fleming, Burley, and Price (2003) argue that this is the result of an implicit assumption that no pedagogical benefits are derived from non-task behavior, hence the reduction of off-task activities in schools such as recess time. This issue has received limited attention in the pedagogical agent literature as well. What happens when a virtual character designed to help a student learn about a topic, introduces off-task comments to a lesson? What happens when a virtual instructor mentions current events? How do learners respond?
These are the issues that I am investigating in a paper published in the current issue of the journal Computers in Human Behavior, as part of my research on the experiences of students who interact with virtual instructors and pedagogical agents. The abstract, citation, and link to the full paper appear below:
In this paper, I investigate the impact of non-task pedagogical agent behavior on learning outcomes, perceptions of agents’ interaction ability, and learner experiences. While quasi-experimental results indicate that while the addition of non-task comments to an on-task tutorial may increase learning and perceptions of the agent’s ability to interact with learners, this increase is not statistically significant. Further addition of non-task comments however, harms learning and perceptions of the agent’s ability to interact with learners in statistically significant ways. Qualitative results reveal that on-task interactions are efficient but impersonal, while non-task interactions were memorable, but distracting. Implications include the potential for non-task interactions to create an uncanny valley effect for agent behavior.
Veletsianos, G. (2012). How do Learners Respond to Pedagogical Agents that Deliver Social-oriented Non-task Messages? Impact on Student Learning, Perceptions, and Experiences. Computers in Human Behavior, 28(1), 275-283.
When creating pedagogical agents for use in online learning environments, designers face numerous challenges. These range from technological (e.g., How do I ensure proper lip-synching when speech is generated in real-time?) to pedagogical (e.g., How do I ensure that the agent provides scaffolding that is appropriate to the students’ needs at a given point in time?) to social (e.g., How can I develop an agent that is sensitive to students’ varying social needs?). While designers deal with these questions frequently and decide on what we deem to be the best approaches to tackle them, we don’t often share the our design thinking with others.
My colleagues and I (Gulz, Haake, Silvervarg, Sjoden, Veletsianos), have just published a book chapter that deals with this issue. In this chapter we discuss design challenges we faced when developing a pedagogical agent, and the steps we took, and decisions we made to tackle those challenges. The challenges we discuss are the following:
- how do we manage learners’ expectations of the agent’s knowledge and social profile,
- how do we deal with learners’ who engage in off-task conversations with an agent, and
- how do we manage abusive comments directed to the agent?
These issues were observed in studies that both Agneta Gulz and myself have independently conducted in the past, and sharing our design thinking with the community sounded like a great idea – hence the publication. A copy of this publication (1.7MB pdf) is provided below:
Gulz, A., Haake, M., Silvervarg, A., Sjoden, B., & Veletsianos, G. (2011). Building a Social Conversational Pedagogical Agent: Design Challenges and Methodological approaches. In Perez-Marin, D., & I. Pascual-Nieto (Eds.), Conversational Agents and Natural Language Interaction: Techniques and Effective Practices (pp. 128-155). IGI Global.
As always, I’d love to hear your input!
I have a new publication out that deals with the degree to which students stereotype virtual characters (short answer: yes they do and this behavior influences learning processes, but sometimes they resist. Or, they say that they resist. It’s a bit more complex than that, but the results present an interesting thinking exercise). This one has been “in the works” for more than a couple of years, but it’s recently been updated because interest on the topic seems to be growing.
Veletsianos, G. (2010). Contextually relevant pedagogical agents: Visual appearance, stereotypes, and first impressions and their impact on learning. Computers & Education, 55(2), 576-585. [pre-print PDF]
Abstract: Humans draw on their stereotypic beliefs to make assumptions about others. Even though prior research has shown that individuals respond socially to media, there is little evidence with regards to learners stereotyping and categorizing pedagogical agents. This study investigated whether learners stereotype a pedagogical agent as being knowledgeable or not knowledgeable and how this acuity influenced learning. Participants were assigned to four experimental conditions differing by agent (scientist or artist) and tutorial type (nanotechnology or punk rock). Quantitative analyses indicated that agents were stereotyped depending on their image and the academic domain under which they functioned. Regardless of tutorial, participants assigned to the artist agent recalled more information than participants assigned to the scientist agent. Learning differences between the groups varied according to whether agent appearance fit the content area under investigation. Qualitative results indicated learner’s stereotypic expectations as well as their unwillingness to draw conclusions based on visual appearance.
It’s standard practice by now that each one of my publications gets its own blog post, not least to alert anyone interested of the availability of the paper and of the fact that they can access a pre-publication copy of it from my publications page.
Our latest paper, which was really fun to write, is:
Veletsianos, G., Heller, R., Overmyer, S., & Procter, M. (2010). Conversational Agents in Virtual Worlds: Bridging Disciplines. British Journal of Educational Technology, 41(1), 123-140. [pdf]
This paper is part of a a BJET special issue focusing on Virtual Worlds that I edited with Prof. Sara de Freitas who’s heading the Serious Games Institute at the University of Conventry. In our introduction to the special issue we note that, “…over the last 30 years, academic disciplines have been encouraged to engage in, and have re-arranged methods that better facilitate, cross-engagement and cross-collaboration.”
Lots can be said about the value of multidisciplinary practice. Yet, due to various barriers that exists across the disciplines, such practice is often limited. Partly to highlight the benefits of multidisciplinary practice and partly to further understanding of issues related to pedagogical agent/avatar design, three colleagues and I engaged in a simple thought experiment: Suppose that you are designing a geriatric avatar with which medical students can hold conversations such that students engage in the diagnosis of certain conditions based on the avatar’s input. How would you design this avatar?
The paper therefore presents the perspective of researchers/practitioners from four disciplines: cognitive
psychology, computing science, learning technologies and engineering. Major challenges are identified, discussed and contrasted across all disciplines. Taken together, the four perspectives draw attention to the quality of agent–user interaction, how theory, practice and research are closely intertwined, and highlight opportunities for cross-fertilisation and collaboration.
Image licensed under a CC commons license by Jungle_Boy.
Another blog post from 35,000 feet, but for a shorter flight, this time to Cyprus. I am spending my time working on a proposal for a book chapter that is to be co-authored with my Swedish colleagues, Agneta, Magnus, Annika, and Bjorn. This chapter discusses how to best design conversational pedagogical agents, in the context of “teachable agents.” Specifically, we are using a design-based research (DBR) approach to discuss how we are addressing the pedagogical agent challenges identified in the literature. This is the first time I am working with teachable agents and I am quite excited about the possibilities. Teachable agents are those that are able to be taught by the learner, and are an example of what Jonassen called cognitive tools in work he has done in the 90’s. Instead of the agent being the domain expert and teaching the “novice learner,” the perspective taken here is one where the agent (or, the Artificial Intelligence engine) is treated as a novice and the learner is treated as someone who has valuable knowledge to contribute. This work occurs in the context of a web-based game, which gives us the ability to play with quite a few parameters relating to the relationship between agents-learners. More about this soon!
One thing that I don’t usually post on this blog is information related to my research on pedagogical agents and virtual characters, which is one of the research strands that I’ve followed for the past 4 years. I am breaking away from that mold by posting this note : )
Specifically, my colleagues (Aaron Doering and Charles Miller) and I developed a research and design framework to guide smooth, natural, and effective communication between learners and pedagogical agents. Our reasons for developing this framework were varied, but after four years of research and design in the field, I became convinced that to push the field forward, we needed guidance. I use the word “guidance” as opposed to the words “rules” or “laws” because we “anticipate that designers, researchers, and instructors will adapt and sculpt the guidelines of the EnALI framework into their unique instructional contexts, ultimately kindling future research and design that will expand the framework foundations.”
The framework (called Enhancing Agent Learner Interactions or EnALI) is grounded on three major theories: socio-cultural notions of learning, cooperative learning, and conflict theory. In this, we have tried to bring a humanist perspective and encourage designers and researchers to move beyond the use of pedagogical agents as dispassionate tools delivering pre-recorded lectures… but I’ll save that information for a different post. The paper is to appear in the Journal of Educational Computing Research as: Veletsianos, G., Miller, C., & Doering, A. (2009). EnALI: A Research and Design Framework for Virtual Characters and Pedagogical Agents. Journal of Educational Computing Research, 41(2), 171-194 [email me for a preprint].
The framework is posted below, but if you want a full explanation of the guidelines, please refer to the paper. As always questions, comments, and critique are appreciated:
1. Pedagogical Agents should be attentive and sensitive to the learner’s needs and wants by:
• Being responsive and reactive to requests for additional and/or expanded information.
• Being redundant.
• Asking for formative and summative feedback.
• Maintaining an appropriate balance between on- and off-task communications.
2. Pedagogical Agents should consider intricacies of the message they send to learners by:
• Making the message appropriate to the receiver’s abilities, experiences, and frame of reference.
• Using congruent verbal and nonverbal messages.
• Clearly owning the message.
• Making messages complete and specific.
• Using descriptive, non-evaluative comments.
• Describing feelings by name, action, or figure of speech.
3. Pedagogical Agents should display socially appropriate demeanor, posture, and representation by:
• Establishing credibility and trustworthiness
• Establishing role and relationship to user/task.
• Being polite and positive (e.g., encouraging, motivating)
• Being expressive (e.g. exhibiting verbal cues in speech).
• Using a visual representation appropriate to content.