Toward Automated Feedback on Teacher Discourse to Enhance Teacher Learning

Jensen, Emily; Dale, Meghan; Donnelly, Patrick J.; Stone, Cathlyn; Kelly, Sean; Godley, Amanda; D’Mello, Sidney K.

doi:10.1145/3313831.3376418

Cited by 48 publications

(41 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…WorldGaze [ 123 ] tries to leverage the existing cameras of smartphones to include gaze information to enrich voice assistants. Jensen et al [ 124 ] present a system to generate feedback for teachers in classrooms automatically. ARMath [ 125 ] is a Deep Learning powered tool for children to discover mathematical concepts with real-life objects with augmented reality.…”

Section: Classifying Hcml Researchmentioning

confidence: 99%

“…Considering the works that focused on the user side, some researchers catered to general end-users or consumers [83,101,200,210], while others on specific end-users. Examples for these include people who need assistance [2,80,[86][87][88][89]96,117,147], medical professionals [57,67,110,192,193], international travelers [50], Amazon Mechanical Turk [60,99], drivers [161,162], musicians [102], teachers [124], students [128], children [72,125], UX designers [65,115,206,209], UI designers [103,111,173], data analysts [97], video creators [84], and game designers [70,165,174,211]. Apart from focusing on a specific user group, some have tried to understand multiple user-perspectives from ML engineers to the end-user [48].…”

Section: The 'Human' In Hcmlmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Kaluarachchi

Reis

Nanayakkara

2021

Sensors

View full text Add to dashboard Cite

After Deep Learning (DL) regained popularity recently, the Artificial Intelligence (AI) or Machine Learning (ML) field is undergoing rapid growth concerning research and real-world application development. Deep Learning has generated complexities in algorithms, and researchers and users have raised concerns regarding the usability and adoptability of Deep Learning systems. These concerns, coupled with the increasing human-AI interactions, have created the emerging field that is Human-Centered Machine Learning (HCML). We present this review paper as an overview and analysis of existing work in HCML related to DL. Firstly, we collaborated with field domain experts to develop a working definition for HCML. Secondly, through a systematic literature review, we analyze and classify 162 publications that fall within HCML. Our classification is based on aspects including contribution type, application area, and focused human categories. Finally, we analyze the topology of the HCML landscape by identifying research gaps, highlighting conflicting interpretations, addressing current challenges, and presenting future HCML research opportunities.

show abstract

Section: Classifying Hcml Researchmentioning

confidence: 99%

Section: The 'Human' In Hcmlmentioning

confidence: 99%

A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Kaluarachchi

Reis

Nanayakkara

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Results from a study of high school English instruction in Western Pennsylvania in 2018 demonstrate a potential relationship between instruction and engagement through teacherreported data. The study was part of a program of research to develop automated methods of teacher observation (see, e.g., Kelly et al, 2018;Stone et al, 2019) and uses the data from 16 teachers featured in Jensen et al (2020). Observational measures of instruction were collected focusing on teacher's discourse practices along with teacher reports of student engagement during each lesson.…”

Section: Using Teacher Reports To Investigate the Relationship Betweementioning

confidence: 99%

Using Lesson-Specific Teacher Reports of Student Engagement to Investigate Innovations in Curriculum and Instruction

Kelly

Abruzzo

2021

Educational Researcher

Self Cite

View full text Add to dashboard Cite

Teacher reports on school organizational functioning, curricular processes, and student engagement are a reliable means of ascertaining valuable information about classroom climate and learning outcomes. Yet, to date, the vast majority of quantitative teacher-reported data, where teachers themselves reach judgments about educational processes, have been summary rather than lesson specific, where teachers evaluate classroom experiences at the moment of instruction. In this study, we examine how lesson-specific teacher survey reports generate insight into the relationship between student engagement and instruction. Results suggest that this underutilized design has significant application for procuring data on within-teacher variability in practice, especially in studies focused on student engagement, active change in teacher practice, and/or teacher buy-in as a mediator of outcomes. Ultimately, we argue that lesson-specific teacher reports may be a valuable tool for researchers in measuring instructional change.

show abstract

“…Instructors benefit from feedback, but intensive human classroom observations are costly, and therefore, the feedback is infrequent. Usually, the received feedback focuses more on evaluating performance than improving obsolete methods [ 17 ]. One traditional solution is “student evaluation of teachers (SETs)” [ 18 ], a survey-based assessment where students mark individual teacher across various parameters on a predefined scale range.…”

Section: Introductionmentioning

confidence: 99%

“…Such manual assessments might not be too reliable as students might only worry about their grades resulting in superficial feedback. Apart from this, the process is time-consuming and the legitimacy of acquired data is still vague [ 17 ]. Marsh [ 19 ] aims to automate instructor's feedback by using an instructor's self-recorded speech recognition while delivering lectures.…”

Section: Introductionmentioning

confidence: 99%

Facial Expression Recognition of Instructor Using Deep Features and Extreme Learning Machine

Bhatti

Jamil

Nida

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Classroom communication involves teacher’s behavior and student’s responses. Extensive research has been done on the analysis of student’s facial expressions, but the impact of instructor’s facial expressions is yet an unexplored area of research. Facial expression recognition has the potential to predict the impact of teacher’s emotions in a classroom environment. Intelligent assessment of instructor behavior during lecture delivery not only might improve the learning environment but also could save time and resources utilized in manual assessment strategies. To address the issue of manual assessment, we propose an instructor’s facial expression recognition approach within a classroom using a feedforward learning model. First, the face is detected from the acquired lecture videos and key frames are selected, discarding all the redundant frames for effective high-level feature extraction. Then, deep features are extracted using multiple convolution neural networks along with parameter tuning which are then fed to a classifier. For fast learning and good generalization of the algorithm, a regularized extreme learning machine (RELM) classifier is employed which classifies five different expressions of the instructor within the classroom. Experiments are conducted on a newly created instructor’s facial expression dataset in classroom environments plus three benchmark facial datasets, i.e., Cohn–Kanade, the Japanese Female Facial Expression (JAFFE) dataset, and the Facial Expression Recognition 2013 (FER2013) dataset. Furthermore, the proposed method is compared with state-of-the-art techniques, traditional classifiers, and convolutional neural models. Experimentation results indicate significant performance gain on parameters such as accuracy, F1-score, and recall.

show abstract

Toward Automated Feedback on Teacher Discourse to Enhance Teacher Learning

Cited by 48 publications

References 41 publications

A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning

Using Lesson-Specific Teacher Reports of Student Engagement to Investigate Innovations in Curriculum and Instruction

Facial Expression Recognition of Instructor Using Deep Features and Extreme Learning Machine

Contact Info

Product

Resources

About