Canada

Classroom discourse is a core medium of instruction - analyzing it can provide a window into teaching and learning as well as driving the development of new tools for improving instruction. We introduce the largest dataset of mathematics classroom transcripts available to researchers, and demonstrate how this data can help improve instruction. The dataset consists of 1,660 45-60 minute long 4th and 5th grade elementary mathematics observations collected by the National Center for Teacher Effectiveness (NCTE) between 2010-2013. The anonymized transcripts represent data from 317 teachers across 4 school districts that serve largely historically marginalized students. The transcripts come with rich metadata, including turn-level annotations for dialogic discourse moves, classroom observation scores, demographic information, survey responses and student test scores. We demonstrate that our natural language processing model, trained on our turn-level annotations, can learn to identify dialogic discourse moves and these moves are correlated with better classroom observation scores and learning outcomes. This dataset opens up several possibilities for researchers, educators and policymakers to learn about and improve K-12 instruction. The dataset can be found at https://github.com/ddemszky/classroom-transcript-analysis

ACL 2023

The NCTE Transcripts: A Dataset of Elementary Math Classroom Transcripts

classroom transcripts

discourse analysis

dataset

## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, keynote presentations, a plenary panel on the place of linguistics and symbolic structures in NLP, and an Industrial Track panel on Careers in NLP.
 
Posters (including Findings of NAACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop and will have an oral session and a poster session.
 
The program also features 6 Tutorials and 26 Workshops.
 
 We wish you a wonderful conference!
 
[The NAACL 2022 Organizing Committee](https://2022.naacl.org/committees/organization/)

NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

workshop paper

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
 The conference will be held in Toronto, Canada, July 9-14, 2023. 
Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
While the impact of Covid has considerably diminished in terms of traveling, obtaining visas to Canada
entails a very long process. Moreover, the global economic conditions pose challenges for many individuals to travel to conferences. Recognizing these circumstances, we know many participants may not be
able to attend the conference in person. Therefore, we are committed to providing a great virtual platform
so everyone has the opportunity to interact with other participants and enjoy the conference. Based on the
current registered participants, approxiately 30% have chosen to attend the conference virtually. Whether
you join us in person or virtually, we sincerely hope everyone has a remarkable conference experience. 
This General Chair’s message is where I express my gratitude to the many individuals who have made
enormous contributions to the conference over the past year.

Read [**ACL 2023 General Chair's message**](https://docs.google.com/document/d/1WobYM7norbG4dI48s75HfJoD89qgX5a_F-6U8AteLSA/edit?usp=sharing/) in full.

##### **[Conference Handbook](https://2023.aclweb.org/downloads/acl2023-handbook.pdf)**

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

Online learning platforms offer a wealth of educational material, but as the amount of content on these platforms grows, students may struggle to determine the most efficient order in which to cover the material to achieve a particular learning objective. In this paper, we propose a feature-based method for identifying pre-requisite dependencies between academic videos. Our approach involves using a transcript engine with a language model to transcribe domain-specific terms and then extracting novel similarity-based features to determine pre-requisite dependencies between video transcripts. This approach succeeds due to the development of a novel corpus of K-12 academic text, which was created using a proposed feature-based document parser. We evaluate our method on hand-annotated datasets for transcript extraction, video pre-requisites determination, and textbook parsing, which we have released. Our method for pre-requisite edge determination shows significant improvement (+4.7%-10.24% F1-score) compared to existing methods.

Auto-req: Automatic detection of pre-requisite dependencies between academic videos

In recent decades, there has been a significant push to leverage technology to aid both teachers and students in the classroom. Language processing advancements have been harnessed to provide better tutoring services, automated feedback to teachers, improved peer-to-peer feedback mechanisms, and measures of student comprehension for reading. Automated question generation systems have the potential to significantly reduce teachers' workload in the latter. In this paper, we compare three different neural architectures for question generation across two types of reading material: narratives and textbooks. For each architecture, we explore the benefits of including question attributes in the input representation. Our models show that a T5 architecture has the best overall performance, with a RougeL score of 0.536 on a narrative corpus and 0.316 on a textbook corpus. We break down the results by attribute and discover that the attribute can improve the quality of some types of generated questions, including Action and Character, but this is not true for all models.

Comparing Neural Question Generation Architectures for Reading Comprehension

L1-L2 parallel dependency treebanks are UD-annotated corpora of learner sentences paired with correction hypotheses. Automatic morphosyntactical annotation has the potential to remove the need for explicit manual error tagging and improve interoperability, but makes it more challenging to locate grammatical errors in the resulting datasets. We therefore propose a novel method for automatically extracting morphosyntactical error patterns and perform a preliminary bilingual evaluation of its first implementation through a similar example retrieval task. The resulting pipeline is also available as a prototype CALL application.

Towards automatically extracting morphosyntactical error patterns from L1-L2 parallel dependency treebanks

Since performing exercises (including, e.g., practice tests) forms a crucial component of learning, and creating such exercises requires non-trivial effort from the teacher, there is a great value in automatic exercise generation in digital tools in education. In this paper, we particularly focus on automatic creation of gapfilling exercises for language learning, specifically grammar exercises. Since providing any annotation in this domain requires human expert effort, we aim to avoid it entirely and explore the task of converting existing texts into new gap-filling exercises, purely based on an example exercise, without explicit instruction or detailed annotation of the intended grammar topics. We contribute (i) a novel neural network architecture specifically designed for aforementioned gap-filling exercise generation task, and (ii) a real-world benchmark dataset for French grammar. We show that our model for this French grammar gap-filling exercise generation outperforms a competitive baseline classifierby8%inF1percentagepoints,achievinganaverageF1scoreof82%. Ourmodelimplementation and the dataset are made publicly available1 to foster future research, thus offering a standardized evaluation and baseline solution of the proposed partially annotated data prediction task in grammar exercise creation.

Learning from Partially Annotated Data: Example-aware Creation of Gap-filling Exercises for Language Learning

The recent advancement of pre-trained Large Language Models (LLMs), such as OpenAI’s ChatGPT, has led to transformative changes across fields. For example, developing intelligent systems in the educational sector that leverage the linguistic capabilities of LLMs demonstrates a visible potential. Though researchers have recently explored how ChatGPT could possibly assist in student learning, few studies have applied these techniques to real-world classroom settings involving teachers and students. In this study, we implement a reading comprehension exercise generation system that provides high-quality and personalized reading materials for middle school English learners in China. Extensive evaluations of the generated reading passages and corresponding exercise questions, conducted both automatically and manually, demonstrate that the system-generated materials are suitable for students and even surpass the quality of existing human-written ones. By incorporating first-hand feedback and suggestions from experienced educators, this study serves as a meaningful pioneering application of ChatGPT, shedding light on the future design and implementation of LLM-based systems in the educational context.

Evaluating Reading Comprehension Exercises Generated by LLMs: A Showcase of ChatGPT in Education Applications

Coaching, which involves classroom observation and expert feedback, is a widespread and fundamental part of teacher training. However, the majority of teachers do not have access to consistent, high quality coaching due to limited resources and access to expertise. We explore whether generative AI could become a cost-effective complement to expert feedback by serving as an automated teacher coach. In doing so, we propose three teacher coaching tasks for generative AI: (A) scoring transcript segments based on classroom observation instruments, (B)
identifying highlights and missed opportunities for good instructional strategies, and (C) providing actionable suggestions for eliciting more student reasoning. 
We recruit expert math teachers to evaluate the zero-shot performance of ChatGPT on each of these tasks for elementary math classroom transcripts. Our results reveal that ChatGPT generates responses that are relevant to improving instruction, but they are often not novel or insightful. For example, $82$\% of the model's suggestions point to places in the transcript where the teacher is already implementing that suggestion. Our work highlights the challenges of producing insightful, novel and truthful feedback for teachers while paving the way for future research to address these obstacles and improve the capacity of generative AI to coach teachers.

Is ChatGPT a Good Teacher Coach? Measuring Zero-Shot Performance For Scoring and Providing Actionable Insights on Classroom Instruction

In English speaking assessment, pretrained large language models (LLMs) such as BERT can score constructed response items as accurately as human raters. Less research has investigated whether LLMs perpetuate or exacerbate biases, which would pose problems for the fairness and validity of the test. This study examines gender and native language (L1) biases in human and automated scores, using an off-the-shelf (OOS) BERT model. Analyses focus on a specific type of bias known as differential item functioning (DIF), which compares examinees of similar English language proficiency. Results show that there is a moderate amount of DIF, based on examinees’ L1 background in grade band 9–12. DIF is higher when scored by an OOS BERT model, indicating that BERT may exacerbate this bias; however, in practical terms, the degree to which BERT exacerbates DIF is very small. Additionally, there is more DIF for longer speaking items and for older examinees, but BERT does not exacerbate these patterns of DIF.

Does BERT Exacerbate Gender or L1 Biases in Automated English Speaking Assessment?

Enriching the quality of early childhood education with interactive math learning at home systems, empowered by recent advances in conversational AI technologies, is slowly becoming a reality. With this motivation, we implement a multimodal dialogue system to support play-based learning experiences at home, guiding kids to master basic math concepts. This work explores Spoken Language Understanding (SLU) pipeline within a task-oriented dialogue system developed for Kid Space, with cascading Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU) components evaluated on our home deployment data with kids going through gamified math learning activities. We validate the advantages of a multi-task architecture for NLU and experiment with a diverse set of pretrained language representations for Intent Recognition and Entity Extraction tasks in the math learning domain. To recognize kids' speech in realistic home environments, we investigate several ASR systems, including the commercial Google Cloud and the latest open-source Whisper solutions with varying model sizes. We evaluate the SLU pipeline by testing our best-performing NLU models on noisy ASR output to inspect the challenges of understanding children for math learning in authentic homes.


Inspecting Spoken Language Understanding from Kids for Basic Math Learning at Home

Socratic questioning is a teaching strategy where the student is guided towards solving a problem on their own, instead of being given the solution directly. In this paper, we introduce a dataset of Socratic conversations where an instructor helps a novice programmer fix buggy solutions to simple computational problems. The dataset is then used for benchmarking the Socratic debugging abilities of GPT-based language models. While GPT-4 is observed to perform much better than GPT-3.5, its precision, and recall still fall short of human expert abilities, motivating further work in this area.

Socratic Questioning of Novice Debuggers: A Benchmark Dataset and Preliminary Evaluations

The increasing reliance on large language models (LLMs) in academic writing has led to a rise in plagiarism. Existing AI-generated text classifiers have limited accuracy and often produce false positives. We propose a novel approach using natural language processing (NLP) techniques, offering quantifiable metrics at both sentence and document levels for easier interpretation by human evaluators. Our method employs a multi-faceted approach, generating multiple paraphrased versions of a given question and inputting them into the LLM to generate answers. By using a contrastive loss function based on cosine similarity, we match generated sentences with those from the student's response. Our approach achieves up to 94% accuracy in classifying human and AI text, providing a robust and adaptable solution for plagiarism detection in academic settings. This method improves with LLM advancements, reducing the need for new model training or reconfiguration, and offers a more transparent way of evaluating and detecting AI-generated text.

Next from ACL 2023

Auto-req: Automatic detection of pre-requisite dependencies between academic videos

Similar lecture

Fine-tuning Transformers with Additional Context to Classify Discursive Moves in Mathematics Classrooms

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