Today’s classrooms are remarkably different from those of yesteryear. In place of individual students responding to the teacher from neat rows of desks, one more typically finds students working in groups on projects, with a teacher circulating among groups. AI applications in learning have been slow to catch up, with most available technologies focusing on personalizing or adapting instruction to learners as isolated individuals. Meanwhile, an established science of Computer Supported Collaborative Learning has come to prominence, with clear implications for how collaborative learning could best be supported. In this contribution, I will consider how intelligence augmentation could evolve to support collaborative learning as well as three signature challenges of this work that could drive AI forward. In conceptualizing collaborative learning, Kirschner and Erkens (2013) provide a useful 3x3 framework in which there are three aspects of learning (cognitive, social and motivational), three levels (community, group/team, and individual) and three kinds of pedagogical supports (discourse-oriented, representation-oriented, and process-oriented). As they engage in this multiply complex space, teachers and learners are both learning to collaborate and collaborating to learn. Further, questions of equity arise as we consider who is able to participate and in which ways. Overall, this analysis helps usmore »
Auto-sending messages in an intelligent orchestration system: A pilot study
FACT (Formative Assessment with Computational Technology) is an intelligent orchestration system. That is, because it helps the teacher manage the workflow of a complicated set of activities in the classroom, it is an orchestration system. Because it conducts tasks-specific and domain-specific analyses of the students’ mathematical products and their group interactions, it is more intelligent than other orchestration systems. From analyzing videos of our iterative development trials, we realized that too many students needed help simultaneously, but the teacher could only visit one group at a time. Thus, we modified FACT to send a few messages to the students directly instead of sending all its advice to the teacher. This paper reports a successful pilot test of auto-sending.
- Award ID(s):
- Publication Date:
- NSF-PAR ID:
- Journal Name:
- Artificial Intelligence in Education
- Page Range or eLocation-ID:
- Sponsoring Org:
- National Science Foundation
More Like this
Hsiao, I. ; Sahebi, S. ; Bouchet, F. ; Vie, J. J. (Ed.)Constructing effective and well-balanced learning groups is important for collaborative learning. Past research explored how group formation policies affect learners’ behaviors and performance. With the different classroom contexts, many group formation policies work in theory, yet their feasibility is rarely investigated in authentic class sessions. In the current work, we define feasibility as the ratio of students being able to find available partners that satisfy a given group formation policy. Informed by user-centered research in K-12 classrooms, we simulated pairing policies on historical data from an intelligent tutoring system (ITS), a process we refer to as SimPairing. As part of the process for designing a pairing orchestration tool, this study contributes insights into the feasibility of four dynamic pairing policies, and how the feasibility varies depending on parameters in the pairing policies or different classes. We found that on average, dynamically pairing students based on their in-the-moment wheel-spinning status can pair most struggling students, even with moderate constraints of restricted pairings. In addition, we found there is a trade-off between the required knowledge heterogeneity and policy feasibility. Furthermore, the feasibility of pairing policies can vary across different classes, suggesting a need for customization regarding pairing policies.
Sosnovsky, S. ; Brusilovsky, P. ; Baraniuk, R. ; Lan, A. (Ed.)An intelligent textbook may be considered to be an interaction layer that lies between the text and the student, helping the student to master the content in the text. The Mobile Fact and Concept Training System (MoFaCTS) is an adaptive instructional system for simple content that has been developed into an interaction layer to mediate textbook instruction and so is being transformed into the Mobile Fact and Concept Textbook System (MoFaCTS). In this project, MoFaCTS is being completely retooled to accept texts from a textbook and to automatically create cloze sentence practice content to help the student learn the material in the text. Additional features in the prototype stage include automatically generated refutational feedback for incorrect cloze responses and a dialog system, which will trigger a short conversation by a tutor to correct conceptual misunderstandings. MoFaCTS administers this content via a web browser, providing the teacher with score reports and class management tools. Because the "optimal practice" module is interchangeable and the cloze content can come from any text, the system is highly configurable for different grade levels, populations, and academic subjects. To foster faster research progress, data export supports the DataShop transaction format, which allows quick analysis of datamore »
Orchestrating the Multidisciplinary Implementation of a Narrative-Centered Learning Environment in Upper Elementary Classrooms.Integration of computational thinking (CT) within STEM subjects is common, although not often at the elementary school level where teachers have minimal experience with CT. We have designed and are refining INFUSECS, a narrative-centered digital learning environment to support upper elementary students’ CT and science knowledge construction as they create digital stories. We used orchestration as our theoretical framework, to examine how elementary teachers planned to approach this multidisciplinary implementation. Through a series of three focus groups, we learned that teachers planned for their students to take notes or utilize other graphic organizers to align the science content with the narrative planning, to engage in collaborative sense-making, and to observe the teacher modeling use of the INFUSECS system. Ultimately, the results have informed the next phase of our research design as we collect teacher and student level data as INFUSECS is utilized in authentic classroom settings.
Developing a measure to capture middle school students’ interpretive understanding of engineering designThis research paper describes the development of an assessment instrument for use with middle school students that provides insight into students’ interpretive understanding by looking at early indicators of developing expertise in students’ responses to solution generation, reflection, and concept demonstration tasks. We begin by detailing a synthetic assessment model that served as the theoretical basis for assessing specific thinking skills. We then describe our process of developing test items by working with a Teacher Design Team (TDT) of instructors in our partner school system to set guidelines that would better orient the assessment in that context and working within the framework of standards and disciplinary core ideas enumerated in the Next Generation Science Standards (NGSS). We next specify our process of refining the assessment from 17 items across three separate item pools to a final total of three open-response items. We then provide evidence for the validity and reliability of the assessment instrument from the standards of (1) content, (2) meaningfulness, (3) generalizability, and (4) instructional sensitivity. As part of the discussion from the standards of generalizability and instructional sensitivity, we detail a study carried out in our partner school system in the fall of 2019. The instrument wasmore »