Natural language Processing can capture communications that represent repetitive interactions like queries related to assignment submissions, courses, examinations etc with ease. Often ending up as mundane tasks for staff, these repetitive admin tasks can be efficiently handled using continuously evolving NLP-based solutions. NLP applications in education help educators to easily scale their digital learning platforms and thereby increase the number of engagements, because the set-up doesn’t demand any heavy infrastructure at learners’ end. Learners have resources like chatbots that they can access easily to have a comfortable learning experience.
You can collaborate with Optimum Data Analytics (ODA) to discover opportunities for transforming your existing teaching systems and revolutionize your training platforms. We bring strong expertise in implementing AI in education sector, with a proven experience of adding value through NLP for education and learning. The best part of using NLP-based teaching solutions is that they do not demand heavy infrastructure and can be implemented in a short period. In fact, an educator can successfully leverage chatbots at just 10% of the cost of building a full-fledged e-learning platform. Data encryption both in transit and at rest, strict access controls, and open data usage regulations are crucial security measures.
To illuminate the concept better, let’s have a look at two of the most top-level techniques used in NLP to process language and information. Natural language processing enables computers to process what we’re saying into commands that it can execute. Find out how the basics of how it works, and how it’s being used to improve our lives. A major drawback of statistical methods is that they require elaborate feature engineering.
Guidance is typically provided through reflection-supporting models (Poldner et al., 2014). Common elements include observation, interpretation, inference on causes, alternative modes of action, and consequences (Korthagen and Kessels, 1999; Poldner et al., 2014; Aeppli and Lötscher, 2016; Ullmann, 2019). Nowak et al. (2019) devised a reflection-supporting model which differentiates reflection elements that are important categories and should be addressed in a written reflection. In this model, preservice teachers are instructed to begin with outlining circumstances of the relevant teaching situation. Next, they describe the teaching situation and evaluate relevant aspects of it with help of their professional knowledge. Finally, the science teachers outline alternatives for their decisions and devise consequences for their professional growth.
To do so, we randomly selected five sentences in each of the 20 lowest and 20 highest rated written reflections based on their text length and their number of sentences that were grouped into domain-specific clusters. Agreement between human labels (“high” versus “low”) with automatically determined labels was calculated. Three independent raters (including the first author) received a spreadsheet with the 5 sampled sentences for the 40 different written reflections.
To improve education for everyone, educators, developers, policymakers, and students must work together to fully realize this potential. With NLP as its compass, eLearning is set to blossom into a symphony of individualized, egalitarian, and cutting-edge natural language processing for enhancing teaching and learning learning experiences that empower learners everywhere. While teachers might change the way they teach from one lesson to another, we averaged across lessons for a teacher to get a portrait of their typical teaching style, as a starting point.
To calculate contextualized embeddings (a), the Python library sentence transformer was used (Reimers and Gurevych, 2019). In line with the exemplary use case by Grootendorst (2020), we (b) utilized uniform manifold approximation and projection (UMAP) to reduce the dimensionality of the embeddings (McInnes et al., 2018). UMAP was found to efficiently reduce high-dimensional data by keeping local structure, which is desirable in our context (Grootendorst, 2020). UMAP involves several crucial hyperparameters that control the resulting embeddings vectors.
Given these challenges, Leonhard and Rihm (2011) content that their content analyses (i.e., reaching human interrater agreement and developing a coding manual) were not scalable across contexts. Ullmann (2019) argued that human resources available in teacher training programs are a major bottleneck to provide preservice teachers opportunities for feedback on their reflection. (3) Even though we found significant group differences between physics and non-physics preservice teachers’ written reflections, we stress that these findings do not reflect the competencies of the students in the respective groups.
This is done to make interpretation of speech consistent across different words that all mean essentially the same thing, which makes NLP processing faster. People have different lengths of pauses between words, and other languages may not have very little in the way of an audible pause between words. Each piece of text is a token, and these tokens are what show up when your speech is processed.
Detailed below are the top 5 benefits of using natural language processing for enhancing teaching and learning. Classroom observations are used in districts across the country to evaluate whether and to what extent teachers are demonstrating teaching practice known to support student engagement and learning. Data generated from classroom observations also provide teachers valuable feedback and support their skill development. In some contexts, such as Washington, D.C., public schools, such information is also used in high-stakes personnel decisions, including whether to retain a teacher. In a paper published this June at ACL’s Workshop on Innovative Use of NLP for Building Educational Applications, the team tested ChatGPT as one possible coaching tool. They found 82% of the model’s suggestions were ideas teachers were already doing, but the tool improved with more tailored prompts.
For example, the bidirectional encoder representations for transformers (BERT) architecture is trained to predict next words in forwards and backwards direction (bidirectional). 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. ML, and pretrained language models in particular, have proven to be effective and efficient methods to advance reflective writing analytics through supervised and unsupervised approaches.
Ultimately, NLP can help to produce better human-computer interactions, as well as provide detailed insights on intent and sentiment. In this blog, we will discuss the most crucial benefits of NLP in teaching and education in detail. Natural Language Processing (NLP) is steadily changing the face of learning systems. The recurring COVID-19 pandemic has only given thrust to the use of intelligent ecosystems for remote learning, with NLP playing a crucial role in driving these developments. Another promising direction that Demszky and Wang have been working on is an NLP system that could act as a teacher’s aide to observe an in-person lesson and offer suggestions to improve. Demszky sees this option almost like a “nonjudgmental coach” that could tailor its suggestions while teachers are still new to the profession and then continue providing in-depth advice even as those teachers become more seasoned.
To automatically filter higher-level reasoning segments, a formerly trained ML model was used to classify segments in teachers’ responses and extract segments on higher-level reflection-related reasoning. Then, a ML-based clustering approach is used to cluster these segments with the goal to extract expert-novice differences in the texts. Natural Language Processing (NLP) is transforming the education sector by revolutionizing teaching and learning experiences.
In RQ1 we used ML models to filter higher-level reasoning segments in physics and non-physics preservice teachers’ written reflections on a video vignette. We found that a previously trained ML model (ML-base) that was reused in the present study yielded acceptable performance to filter higher-level reasoning segments. This performance could be noticeably improved by further finetuning the ML model with training data from the non-physics preservice teachers to reach substantial human-machine agreement (ML-finetuned).
However, necessary covariates need to be collected which was not part of this study. Differences between both contexts are already apparent when considering segments (i.e., sentences) per document and mean words per segment. The non-physics preservice teachers scored in the lower half of the distributions for segments per document, 7.69 and 17.7, respectively whereas the median (SD) values were 9.2 (3.3) and 18.6 (6.3).