Title: Data Work in Education: Enacting and Negotiating Care and Control in Teachers' Use of Data-Driven Classroom Surveillance Technology
Today, teachers have been increasingly relying on data-driven technologies to track and monitor student behavior data for classroom management. Drawing insights from interviews with 20 K--8 teachers, in this paper we unpack how teachers enacted both care and control through their data work in collecting, interpreting, and using student behavior data. In this process, teachers found themselves subject to surveilling gazes from parents, school administrators, and students. As a result, teachers had to manipulate the student behavior data to navigate the balance between presenting a professional image to surveillants and enacting care/control that they deemed appropriate. In this paper we locate two nuanced forms of teachers' data work that have been under-studied in CSCW: (1) data work as recontextualizing meanings and (2) data work as resisting surveillance. We discuss teachers' struggle over (in)visibility and their negotiation of autonomy and subjectivity in these two forms of data work. We highlight the importance of foregrounding and making space for informal data workers' (in our case, teachers') resistance and negotiation of autonomy in light of datafication. more »« less
Holbert, N.; Xu, C.
(, Proceedings of the 15th International Conference of the Learning Sciences—ICLS 2021)
null; null; null
(Ed.)
In this poster we describe Make with Data, a two-year project that invites teachers and students from public high schools to work with professional data scientists and open-source data to explore issues important to their local community. While the negotiation of the personal and the quantitative resulted in tensions, Make with Data students found their personal experiences a useful tool for adding context and complexity to the phenomena being studied.
Akhuseyinoglu, Kamil; Brusilovsky, Peter
(, the 29th ACM Conference on User Modeling, Adaptation and Personalization)
null
(Ed.)
Individual differences have been recognized as an important factor in the learning process. However, there are few successes in using known dimensions of individual differences in solving an important problem of predicting student performance and engagement in online learning. At the same time, learning analytics research has demonstrated that the large volume of learning data collected by modern e-learning systems could be used to recognize student behavior patterns and could be used to connect these patterns with measures of student performance. Our paper attempts to bridge these two research directions. By applying a sequence mining approach to a large volume of learner data collected by an online learning system, we build models of student learning behavior. However, instead of following modern work on behavior mining (i.e., using this behavior directly for performance prediction tasks), we attempt to follow traditional work on modeling individual differences in quantifying this behavior on a latent data-driven personality scale. Our research shows that this data-driven model of individual differences performs significantly better than several traditional models of individual differences in predicting important parameters of the learning process, such as success and engagement.
In education, intelligent learning environments allow students to choose how to tackle open-ended tasks while monitoring performance and behavior, allowing for the creation of adaptive support to help students overcome challenges. Timely feedback is critical to aid students’ progression toward learning and improved problem-solving. Feedback on text-based student responses can be delayed when teachers are overloaded with work. Automated evaluation can provide quick student feedback while easing the manual evaluation burden for teachers in areas with a high teacher-to-student ratio. Current methods of evaluating student essay responses to questions have included transformer-based natural language processing models with varying degrees of success. One main challenge in training these models is the scarcity of data for student-generated data. Larger volumes of training data are needed to create models that perform at a sufficient level of accuracy. Some studies have vast data, but large quantities are difficult to obtain when educational studies involve student-generated text. To overcome this data scarcity issue, text augmentation techniques have been employed to balance and expand the data set so that models can be trained with higher accuracy, leading to more reliable evaluation and categorization of student answers to aid teachers in the student’s learning progression. This paper examines the text-generating AI model, GPT-3.5, to determine if prompt-based text-generation methods are viable for generating additional text to supplement small sets of student responses for machine learning model training. We augmented student responses across two domains using GPT-3.5 completions and used that data to train a multilingual BERT model. Our results show that text generation can improve model performance on small data sets over simple self-augmentation.
Lyle, Angela M; Peurach, Donald J
(, Research in Education)
Historically, teachers had been delegated the primary responsibility for the organization and management of classroom instruction in US public schools. While this delegation afforded teachers professional autonomy in their work, it has also resulted in disparities in students’ educational experiences and outcomes within and between classrooms, schools, and systems. In the effort to improve instruction and reduce disparities for students on a large scale, one reform effort in the US has focused on building instructionally focused education systems (IFESs) where central office and school leaders collaborate with teachers to organize and manage instruction. These efforts are playing out in a variety of contexts in the US, including in public school districts, non-profits, and other educational networks, and it is shifting how teachers carry out the day-to-day work of instruction. In this comparative case study, we investigate two IFESs in which efforts to improve instruction pushed against historic norms of teacher autonomy. We found that these new systems are not at odds with teacher autonomy, but rather these systems reflect a transition to more interdependent notions of teacher autonomy.
Christensen, Rhonda; Knezek, Gerald
(, Journal of interactive learning research)
Ferdig, R; Gandolfi, E; Baumgartner, E
(Ed.)
Student engagement, cultural identity and voice in school have been shown to have measurable influence on student learning. While many factors may affect student dispositions, teachers likely have the most direct impact on the dispositions related to classroom engagement and student voice. Measures of student perceptions of their teachers’ cultural engagement, cultural teaching practices and the students’ own engagement and voice in schooling are included in this paper. Data from 822 grade 3 – 12 students of teachers who participated in a simulated teaching environment intended to improve equitable teaching practices revealed significant pre-post changes for measures of voice and engagement. The data also showed significant differences by gender and ethnicity.
Lu, Alex Jiahong, Dillahunt, Tawanna R., Marcu, Gabriela, and Ackerman, Mark S. Data Work in Education: Enacting and Negotiating Care and Control in Teachers' Use of Data-Driven Classroom Surveillance Technology. Retrieved from https://par.nsf.gov/biblio/10332274. Proceedings of the ACM on Human-Computer Interaction 5.CSCW2 Web. doi:10.1145/3479596.
Lu, Alex Jiahong, Dillahunt, Tawanna R., Marcu, Gabriela, & Ackerman, Mark S. Data Work in Education: Enacting and Negotiating Care and Control in Teachers' Use of Data-Driven Classroom Surveillance Technology. Proceedings of the ACM on Human-Computer Interaction, 5 (CSCW2). Retrieved from https://par.nsf.gov/biblio/10332274. https://doi.org/10.1145/3479596
Lu, Alex Jiahong, Dillahunt, Tawanna R., Marcu, Gabriela, and Ackerman, Mark S.
"Data Work in Education: Enacting and Negotiating Care and Control in Teachers' Use of Data-Driven Classroom Surveillance Technology". Proceedings of the ACM on Human-Computer Interaction 5 (CSCW2). Country unknown/Code not available. https://doi.org/10.1145/3479596.https://par.nsf.gov/biblio/10332274.
@article{osti_10332274,
place = {Country unknown/Code not available},
title = {Data Work in Education: Enacting and Negotiating Care and Control in Teachers' Use of Data-Driven Classroom Surveillance Technology},
url = {https://par.nsf.gov/biblio/10332274},
DOI = {10.1145/3479596},
abstractNote = {Today, teachers have been increasingly relying on data-driven technologies to track and monitor student behavior data for classroom management. Drawing insights from interviews with 20 K--8 teachers, in this paper we unpack how teachers enacted both care and control through their data work in collecting, interpreting, and using student behavior data. In this process, teachers found themselves subject to surveilling gazes from parents, school administrators, and students. As a result, teachers had to manipulate the student behavior data to navigate the balance between presenting a professional image to surveillants and enacting care/control that they deemed appropriate. In this paper we locate two nuanced forms of teachers' data work that have been under-studied in CSCW: (1) data work as recontextualizing meanings and (2) data work as resisting surveillance. We discuss teachers' struggle over (in)visibility and their negotiation of autonomy and subjectivity in these two forms of data work. We highlight the importance of foregrounding and making space for informal data workers' (in our case, teachers') resistance and negotiation of autonomy in light of datafication.},
journal = {Proceedings of the ACM on Human-Computer Interaction},
volume = {5},
number = {CSCW2},
author = {Lu, Alex Jiahong and Dillahunt, Tawanna R. and Marcu, Gabriela and Ackerman, Mark S.},
}
Warning: Leaving National Science Foundation Website
You are now leaving the National Science Foundation website to go to a non-government website.
Website:
NSF takes no responsibility for and exercises no control over the views expressed or the accuracy of
the information contained on this site. Also be aware that NSF's privacy policy does not apply to this site.
An official website of the United States government
