# Supporting Cognitive and Emotional Empathic Writing of Students

arXiv: https://arxiv.org/abs/2105.14815 · HTML: https://mlsanity.com/paper/2105.14815/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 62 references that are in the map.
- First posted on arXiv 31 May 2021.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support](https://mlsanity.com/paper/2009.08441/) (2020)
- [Parsing Argumentation Structures in Persuasive Essays](https://mlsanity.com/paper/1604.07370/) (2016)
- [EmotionLines: An Emotion Corpus of Multi-Party Conversations](https://mlsanity.com/paper/1802.08379/) (2018)
- [Modeling Empathy and Distress in Reaction to News Stories](https://mlsanity.com/paper/1808.10399/) (2018)
- [Emotion Intensities in Tweets](https://mlsanity.com/paper/1708.03696/) (2017)
- [SemEval-2016 Task 4: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.01973/) (2019)
- [Annotating and Modeling Empathy in Spoken Conversations](https://mlsanity.com/paper/1705.04839/) (2017)
- [Emotion Representation Mapping for Automatic Lexicon Construction (Mostly) Performs on Human Level](https://mlsanity.com/paper/1806.08890/) (2018)
- [A Corpus for Argumentative Writing Support in German](https://mlsanity.com/paper/2010.13674/) (2020)

## Built on by (most cited first, 8 of 8)

- [A Design Space for Intelligent and Interactive Writing Assistants](https://mlsanity.com/paper/2403.14117/) (2024)
- [Human-AI Collaboration Enables More Empathic Conversations in Text-based Peer-to-Peer Mental Health Support](https://mlsanity.com/paper/2203.15144/) (2022)
- [Bias at a Second Glance: A Deep Dive into Bias for German Educational Peer-Review Data Modeling](https://mlsanity.com/paper/2209.10335/) (2022)
- [A Critical Reflection and Forward Perspective on Empathy and Natural Language Processing](https://mlsanity.com/paper/2210.16604/) (2022)
- [Claim Optimization in Computational Argumentation](https://mlsanity.com/paper/2212.08913/) (2022)
- [Investigating Stylistic Profiles for the Task of Empathy Classification in Medical Narrative Essays](https://mlsanity.com/paper/2302.01839/) (2023)
- [Revisiting the Role of Similarity and Dissimilarity in Best Counter Argument Retrieval](https://mlsanity.com/paper/2304.08807/) (2023)
- [Understanding Revision Behavior in Adaptive Writing Support Systems for Education](https://mlsanity.com/paper/2306.10304/) (2023)

## Abstract (arXiv)

We present an annotation approach to capturing emotional and cognitive empathy in student-written peer reviews on business models in German. We propose an annotation scheme that allows us to model emotional and cognitive empathy scores based on three types of review components. Also, we conducted an annotation study with three annotators based on 92 student essays to evaluate our annotation scheme. The obtained inter-rater agreement of α=0.79 for the components and the multi-π=0.41 for the empathy scores indicate that the proposed annotation scheme successfully guides annotators to a substantial to moderate agreement. Moreover, we trained predictive models to detect the annotated empathy structures and embedded them in an adaptive writing support system for students to receive individual empathy feedback independent of an instructor, time, and location. We evaluated our tool in a peer learning exercise with 58 students and found promising results for perceived empathy skill learning, perceived feedback accuracy, and intention to use. Finally, we present our freely available corpus of 500 empathy-annotated, student-written peer reviews on business models and our annotation guidelines to encourage future research on the design and development of empathy support systems.