More Like this

1. Automated classification of activities in classroom videos.

   https://doi.org/10.1016/j.caeai.2024.100207  

   Foster, Jonathan K; Korban, Matthew; Youngs, Peter; Watson, Ginger S; Acton, Scott T (June 2024, Computers and Education: Artificial Intelligence)

   Hwang, Gwo-Jen; Xie, Haoran; Wah, Benjamin; Gasevic, Dragan (Ed.)

   Classroom videos are a common source of data for educational researchers studying classroom interactions as well as a resource for teacher education and professional development. Over the last several decades emerging technologies have been applied to classroom videos to record, transcribe, and analyze classroom interactions. With the rise of machine learning, we report on the development and validation of neural networks to classify instructional activities using video signals, without analyzing speech or audio features, from a large corpus of nearly 250 h of classroom videos from elementary mathematics and English language arts instruction. Results indicated that the neural networks performed fairly-well in detecting instructional activities, at diverse levels of complexity, as compared to human raters. For instance, one neural network achieved over 80% accuracy in detecting four common activity types: whole class activity, small group activity, individual activity, and transition. An issue that was not addressed in this study was whether the fine-grained and agnostic instructional activities detected by the neural networks could scale up to supply information about features of instructional quality. Future applications of these neural networks may enable more efficient cataloguing and analysis of classroom videos at scale and the generation of fine-grained data about the classroom environment to inform potential implications for teaching and learning. 

   more » « less
2. Instructional Activity Recognition Using A Transformer Network with Multi-Semantic Attention

   https://doi.org/10.1109/SSIAI59505.2024.10508634  

   Korban, Matthew; Acton, Scott T; Youngs, Peter; Foster, Jonathan (March 2024, IEEE)

   Korban, Matthew; Acton, Scott T; Youngs, Peter; Foster, Jonathan (Ed.)

   Instructional activity recognition is an analytical tool for the observation of classroom education. One of the primary challenges in this domain is dealing with the intri- cate and heterogeneous interactions between teachers, students, and instructional objects. To address these complex dynamics, we present an innovative activity recognition pipeline designed explicitly for instructional videos, leveraging a multi-semantic attention mechanism. Our novel pipeline uses a transformer network that incorporates several types of instructional seman- tic attention, including teacher-to-students, students-to-students, teacher-to-object, and students-to-object relationships. This com- prehensive approach allows us to classify various interactive activity labels effectively. The effectiveness of our proposed algo- rithm is demonstrated through its evaluation on our annotated instructional activity dataset. 

   more » « less
3. A multi-modal transformer network for action detection.

   https://doi.org/10.1016/j.patcog.2023.109713  

   Korban, M.; Youngs, P.; Acton, S (October 2023, Pattern recognition)

   Hancock, E. (Ed.)

   This paper proposes a multi-modal transformer network for detecting actions in untrimmed videos. To enrich the action features, our transformer network utilizes a novel multi-modal attention mechanism that captures the correlations between different combinations of spa- tial and motion modalities. Exploring such correlations for actions effectively has not been explored before. We also suggest an algorithm to correct the motion distortion caused by camera movements. Such motion distortion severely reduces the expressive power of motion features represented by optical flow vectors. We also introduce a new instructional activity dataset that includes classroom videos from K-12 schools. We conduct comprehensive ex- periments to evaluate the performance of different approaches on our dataset. Our proposed algorithm outperforms the state-of-the-art methods on two public benchmarks, THUMOS14 and ActivityNet, and our instructional activity dataset. 

   more » « less
4. Optimizing preservice teacher learning with digital teaching simulations: Comparing teachers’ self-assessment and scoring from trained raters

   Kinsey, Devon; Mikeksa, JN (March 2024, SITE)

   In this study, we explored how the use of an online digital teaching simulation impacts preservice teacher (PST) learning. We describe the overall implementation of an online practice suite of digital teaching simulations into five teacher education courses. Specifically, we detail the avatar-based simulation activity in which PSTs facilitate a discussion focused on argumentation with five student avatars controlled by a trained actor in the Mursion simulated classroom environment. The present study examines PSTs’ self-assessment of their performance facilitating a discussion in this simulated classroom compared to rubric-level scores assigned by trained raters. We share findings from our analysis of survey data regarding 47 PSTs’ perceptions about their experience with the simulated classroom, specifically how successful they thought they were across five dimensions of facilitating argumentation-focused discussions. Findings suggest that the PSTs’ self-assessment tended to align with the scores assigned from trained raters. However, when the PSTs’ self-assessment did not align with the raters’ scoring, PSTs tended to perceive their discussion facilitation more positively than the raters’ scores indicated, which suggests the need for additional support to help PSTs identify and attend to specific areas for improvement. Findings provide support for the use of both self-assessment and scoring from trained raters to optimize PST learning with digital teaching simulations. 

   more » « less
5. GTAs’ Conceptualization of Active Learning in Undergraduate Mathematical Sciences Courses

   Houston, Scotty; Gomez, Josias; Webb, Jessica; Harrell-Williams, Leigh (January 2020, 23rd Annual Conference on Research in Undergraduate Mathematics Education Conference (RUME))

   Karunakaran, Shiv Smith; Reed, Zackery; Higgins, Abigail (Ed.)

   Throughout the past few decades, the term active learning has been used to describe a variety of classroom instructional techniques and pedagogy. In this poster, we explore the conceptualization and implementation of active learning strategies for graduate teaching assistants (GTAs) in three Departments of Mathematical Sciences at the start of a funded project evaluating a multifaceted GTA training model. 

   more » « less