The Next Generation Science Standards and the National Research Council recognize systems thinking as an essential skill to address the global challenges of the 21st century. But the habits of mind needed to understand complex systems are not readily learned through traditional approaches. Recently large-scale interactive multi-user immersive simulations are being used to expose the learners to diverse topics that emulate real-world complex systems phenomena. These modern-day mixed reality simulations are unique in that the learners are an integral part of the evolving dynamics. The decisions they make and the actions that follow, collectively impact the simulated complex system, much like any real-world complex system. But the learners have difficulty understanding these coupled complex systems processes, and often get “lost” or “stuck,” and need help navigating the problem space. Formative feedback is the traditional way educators support learners during problem solving. Traditional goal-based and learner-centered approaches don’t scale well to environments that allow learners to explore multiple goals or solutions, and multiple solution paths (Mallavarapu & Lyons, 2020). In this work, we reconceptualize formative feedback for complex systems-based learning environments, formative fugues, (a term derived from music by Reitman, 1964) to allow learners to make informed decisions about their own exploration paths. We discuss a novel computational approach that employs causal inference and pattern matching to characterize the exploration paths of prior learners and generate situationally relevant formative feedback. We extract formative fugues from the data collected from an ecological complex systems simulation installed at a museum. The extracted feedback does not presume the goals of the learners, but helps the learners understand what choices and events led to the current state of the problem space, and what paths forward are possible. We conclude with a discussion of implications of using formative fugues for complex systems education.
more »
« less
Investigating Differential Error Types Between Human and Simulated Learners
Simulated learners represent computational theories of human learning that can be used to evaluate educational technologies, provide practice opportunities for teachers, and advance our theoretical understanding of human learning. A key challenge in working with simulated learners is evaluating the accuracy of the simulation compared to the behavior of real human students. One way this evaluation is done is by comparing the error-rate learning curves from a population of human learners and a corresponding set of simulated learners. In this paper, we argue that this approach misses an opportunity to more accurately capture nuances in learning by treating all errors as the same. We present a simulated learner system, the Apprentice Learner (AL) Architecture, and use this more nuanced evaluation to demonstrate ways in which it does and does not explain and accurately predict student learning in terms of the reduction of different kinds of errors over time as it learns, as human students do, from an Intelligent Tutoring System (ITS).
more »
« less
- Award ID(s):
- 1824257
- NSF-PAR ID:
- 10174644
- Date Published:
- Journal Name:
- Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science
- Volume:
- 12163
- Page Range / eLocation ID:
- 586-597
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
A 2019 report from the National Academies on Minority Serving Institutions (MSIs) concluded that MSIs need to change their culture to successfully serve students with marginalized racial and/or ethnic identities. The report recommends institutional responsiveness to meet students “where they are,” metaphorically, creating supportive campus environments and providing tailored academic and social support structures. In recent years, the faculty, staff, and administrators at California State University, Los Angeles have made significant efforts to enhance student success through multiple initiatives including a summer bridge program, first-year in engineering program, etc. However, it has become clear that more profound changes are needed to create a culture that meets students “where they are.” In 2020, we were awarded NSF support for Eco-STEM, an initiative designed to change a system that demands "college-ready" students into one that is "student-ready." Aimed at shifting the deficit mindset prevailing in engineering education, the Eco-STEM project embraces an asset-based ecosystem model that thinks of education as cultivation, and ideas as seeds we are planting, rather than a system of standards and quality checks. This significant paradigm and culture transformation is accomplished through: 1) The Eco-STEM Faculty Fellows’ Community of Practice (CoP), which employs critically reflective dialogue[ ][ ] to enhance the learning environment using asset-based learner-centered instructional approaches; 2) A Leadership CoP with department chairs and program directors that guides cultural change at the department/program level; 3) A Facilitators’ CoP that prepares facilitators to lead, sustain, update, and expand the Faculty and Leadership CoPs; 4) Reform of the teaching evaluation system to sustain the cultural changes. This paper presents the progress and preliminary findings of the Eco-STEM project. During the first project year, the project team formulated the curriculum for the Faculty CoP with a focus on inclusive pedagogy, community cultural wealth, and community building, developed a classroom peer observation tool to provide formative data for teaching reflection, and designed research inquiry tools. The latter investigates the following research questions: 1) To what extent do the Eco-STEM CoPs effectively shift the mental models of participants from a factory-like model to an ecosystem model of education? 2) To what extent does this shift support an emphasis on the assets of our students, faculty, and staff members and, in turn, allow for enhanced motivation, excellence and success? 3) To what extent do new faculty assessment tools designed to provide feedback that reflects ecosystem-centric principles and values allow for individuals within the system to thrive? In Fall 2021, the first cohort of Eco-STEM Faculty Fellows were recruited, and rich conversations and in-depth reflections in our CoP meetings indicated Fellows’ positive responses to both the CoP curriculum and facilitation practices. This paper offers a work-in-progress introduction to the Eco-STEM project, including the Faculty CoP, the classroom peer observation tool, and the proposed research instruments. We hope this work will cultivate broader conversations within the engineering education research community about cultural change in engineering education and methods towards its implementation.more » « less
-
Teachable agents are pedagogical agents that employ the ‘learning-by-teaching’ strategy, which facilitates learning by encouraging students to construct explanations, reflect on misconceptions, and elaborate on what they know. Teachable agents present unique opportunities to maximize the benefits of a ‘learning-by-teaching’ experience. For example, teachable agents can provide socio-emotional support to learners, influencing learner self-efficacy and motivation, and increasing learning. Prior work has found that a teachable agent which engages learners socially through social dialogue and paraverbal adaptation on pitch can have positive effects on rapport and learning. In this work, we introduce Emma, a teachable robotic agent that can speak socially and adapt on both pitch and loudness. Based on the phenomenon of entrainment, multi-feature adaptation on tone and loudness has been found in human-human interactions to be highly correlated to learning and social engagement. In a study with 48 middle school participants, we performed a novel exploration of how multi-feature adaptation can influence learner rapport and learning as an independent social behavior and combined with social dialogue. We found significantly more rapport for Emma when the robot both adapted and spoke socially than when Emma only adapted and indications of a similar trend for learning. Additionally, it appears that an individual’s initial comfort level with robots may influence how they respond to such behavior, suggesting that for individuals who are more comfortable interacting with robots, social behavior may have a more positive influence.more » « less
-
Teachable agents are pedagogical agents that employ the 'learning-by-teaching' strategy, which facilitates learning by encouraging students to construct explanations, reflect on misconceptions, and elaborate on what they know. Teachable agents present unique opportunities to maximize the benefits of a 'learning-by-teaching' experience. For example, teachable agents can provide socio-emotional support to learners, influencing learner self-efficacy and motivation, and increasing learning. Prior work has found that a teachable agent which engages learners socially through social dialogue and paraverbal adaptation on pitch can have positive effects on rapport and learning. In this work, we introduce Emma, a teachable robotic agent that can speak socially and adapt on both pitch and loudness. Based on the phenomenon of entrainment, multi-feature adaptation on tone and loudness has been found in human-human interactions to be highly correlated to learning and social engagement. In a study with 48 middle school participants, we performed a novel exploration of how multi-feature adaptation can influence learner rapport and learning as an independent social behavior and combined with social dialogue. We found significantly more rapport for Emma when the robot both adapted and spoke socially than when Emma only adapted and indications of a similar trend for learning. Additionally, it appears that an individual’s initial comfort level with robots may influence how they respond to such behavior, suggesting that for individuals who are more comfortable interacting with robots, social behavior may have a more positive influence.more » « less
-
null (Ed.)People who grow up speaking a language without lexical tones typically find it difficult to master tonal languages after childhood. Accumulating research suggests that much of the challenge for these second language (L2) speakers has to do not with identification of the tones themselves, but with the bindings between tones and lexical units. The question that remains open is how much of these lexical binding problems are problems of encoding (incomplete knowledge of the tone-to-word relations) vs. retrieval (failure to access those relations in online processing). While recent work using lexical decision tasks suggests that both may play a role, one issue is that failure on a lexical decision task may reflect a lack of learner confidence about what is not a word, rather than non-native representation or processing of known words. Here we provide complementary evidence using a picture- phonology matching paradigm in Mandarin in which participants decide whether or not a spoken target matches a specific image, with concurrent event-related potential (ERP) recording to provide potential insight into differences in L1 and L2 tone processing strategies. As in the lexical decision case, we find that advanced L2 learners show a clear disadvantage in accurately identifying tone mismatched targets relative to vowel mismatched targets. We explore the contribution of incomplete/uncertain lexical knowledge to this performance disadvantage by examining individual data from an explicit tone knowledge post-test. Results suggest that explicit tone word knowledge and confidence explains some but not all of the errors in picture-phonology matching. Analysis of ERPs from correct trials shows some differences in the strength of L1 and L2 responses, but does not provide clear evidence toward differences in processing that could explain the L2 disadvantage for tones. In sum, these results converge with previous evidence from lexical decision tasks in showing that advanced L2 listeners continue to have difficulties with lexical tone recognition, and in suggesting that these difficulties reflect problems both in encoding lexical tone knowledge and in retrieving that knowledge in real time.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-52237-7_47
