Utilizing BERT as the backbone for further NLP tasks such as classification typically improved performance (Devlin et al., 2018). Et al. (2022) could show that utilizing BERT for reflective writing analytics in science teacher education could boost classification accuracy and generalizability. Also, Carpenter et al. (2020) showed that pretrained language models yielded the best classification performance for reflective depth of middle-school students’ responses in a game-based microbiology learning environment. Pretrained language models could not only help to improve classification accuracy, but also to identify and cluster science teachers’ responses in unsupervised ML approaches.
While typically rather holistic, summative assessment is used to score writing assignments, ML and NLP methods have been argued to facilitate analytical, formative assessment. Analytical, formative assessment would be desirable given that it can be used to provide feedback on how to improve task performance, rather than text quality. In this study we explored potentials and challenges of utilizing ML and NLP to advance formative assessment in science teacher education for reflective writing. In our case, the input segments were sentences extracted through the spaCy library in Python (Honnibal and Montani, 2017) from the written reflections that are manually coded according to the elements of the reflection-supporting model (see Figure 2). The words were tokenized (word-piece tokenization) based on a predefined vocabulary into word pieces in order to avoid very rare words to occur in the language model (see Figure 2). This has been found to improve model performance for text translation tasks (Wu et al., 2016).
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However, degree of human-machine agreement ranged from fair to poor agreement, depending on the familiarity of the human rater with the research context. Familiarity with the written reflections and the standardized video vignette seemingly helped to raise human-machine agreement. Moreover, even the human raters did not agree with each other on the text quality. Hence, the text excerpts are probably too short to provide all the necessary information to determine text quality. An extended validation procedure would be needed to determine to what extent simple criteria such as document length and addressed physics topics alone could be used to automatically score preservice teachers written reflections.
In this article, we will explore some exciting NLP projects for education that can significantly impact the way students learn and teachers teach. These projects leverage the power of NLP to improve various aspects of education, including language learning, content analysis, personalized feedback, and student engagement. In particular, personal writing was found to be effective for learning (Smyth, 1998; Bangert-Drowns et al., 2004). Consequently, science education researchers used writing assignments to facilitate development of conceptual understanding, critical thinking, and reflective thinking, among others, and for assessment. Facilitating conceptual understanding and writing quality was accomplished with the Science Writing Heuristic (SWH).
Enhancing Learning and Classroom Experience with Natural Language Processing (NLP) Projects for Education
Kost (2019) was forced to apply consensus coding for analyzing physics teachers’ reflections, because the human interrater agreements were rather low in the context of classifying physics teachers’ reflections. Also in a setting in science education, Abels (2011) ended up with a coding process that comprised six stages with many raters involved to reach agreement. Agreements between the raters in the first five circles were rather low, caused by the inferential nature of the task. Kember et al. (1999) first reached reasonable agreement for eight raters, and later acceptable agreement between four raters for level of reflection. They noticed that disagreements resulted from different interpretations from the written reflections and they suggest to only employ project-intern raters. Sparks-Langer et al. (1990) note about their coding that “[u]sing a one-level difference in codes as acceptable, the two raters’ interview scores matched in 81 percent of the cases.
The future of NLP looks promising, with ongoing advancements in machine learning and deep learning techniques. It is expected that NLP will continue to enhance human-computer interaction, making voice assistants and chatbots more intelligent and capable of natural conversations. NLP will also play a vital role in the field of healthcare, enabling analysis of medical records, patient data, and research papers to improve diagnosis and treatment methods.
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The type-token-ratio was also lowest for the non-science context sample, 0.22, against a median (SD) values of 0.40 (0.10). This means that these students used a more unspecific language (i.e., less unique words). Linguists posit that the type-token-ratio can be indicative of the acquired vocabulary by a person (Youmans, 1990). Hence, this can be seen as evidence that the non-science students had less domain-specific vocabulary.
- Moreover, control of variables is an intricate concept that is even more difficult to implement in practice—especially with short experiments that are meant to demonstrate phenomena rather than experiments where the entire experimental cycle is implemented.
- The extracted topics could be distinguished to relate to more general and more physics-specific contents in the video vignette.
- The versatility of language models to form the backbone for different language-related tasks and the importance of writing assignments in science education motivate this path to be further explored.
- To begin preparing now, start understanding your text data assets and the variety of cognitive tasks involved in different roles in your organization.
- Less represented training examples result in performance decreases, such that training data typically more accurately captures well-represented relationships (Christian, 2021).
- The original suggestion itself wasn’t perfect, but it reminded me of some critical topics that I had overlooked, and I revised the article accordingly.
Written reflections are mostly scored in holistic, summative form (Poldner et al., 2014). Holistic assessment are characterized by aggregate evaluations of language and ideas that oftentimes contain several conceptual components (Jescovitch natural language processing for enhancing teaching and learning et al., 2021). NLP utilizes a combination of linguistics, computer science, and machine learning techniques. It involves pre-processing steps such as text tokenization, where sentences or words are split into individual units.
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NLP is continuously evolving, thanks to advancements in machine learning, deep learning, and increased availability of large-scale datasets. Recent progress in neural networks has led to significant improvements in language understanding tasks. Transfer learning approaches, leveraging pre-trained language models, have further enhanced the performance of NLP systems. Furthermore, ongoing research and collaborations contribute to the development of new techniques and algorithms, continually pushing the boundaries of what NLP can achieve. Noticing and interpreting learning-relevant classroom events is then linked with science teachers’ professional knowledge and beliefs (Carlson et al., 2019; Wulff P. et al., 2022).
We then further finetune this ML model with data from the non-physics context (ML-finetuned) and examine if classification performance can be improved. Evaluating the performance of the ML models will be achieved through cross-validation where generalizability of the ML model is tested by applying it to unseen test data. In cross-validation, the ML model is trained on a training dataset and tested on a held-out test dataset that the model did not see in the training phase.
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These systems employ techniques such as named-entity recognition, syntactic parsing, and semantic matching to understand and respond to natural language questions. By providing accurate and relevant answers, these systems support independent learning and enable students to explore topics beyond their textbooks. NLP techniques can be employed to analyze, summarize, and extract key insights from vast amounts of educational content.
Brainstorming tasks are great for generating ideas or identifying overlooked topics, and despite the noisy results and barriers to adoption, they are currently valuable for a variety of situations. Yet, of all the tasks Elicit offers, I find the literature review the most useful. Because Elicit is an AI research assistant, this is sort of its bread-and-butter, and when I need to start digging into a new research topic, it has become my go-to resource.
Examples of Natural Language Processing
Marketers are always looking for ways to analyze customers, and NLP helps them do so through market intelligence. Market intelligence can hunt through unstructured data for patterns that help identify trends that marketers can use to their advantage, including keywords and competitor interactions. Using this information, marketers can help companies refine their marketing approach and make a bigger impact. In addition to making sure you don’t text the wrong word to your friends and colleagues, NLP can also auto correct your misspelled words in programs such as Microsoft Word.