Programming with Natural Languages: A Survey

Thomas, Julien Joseph; Suresh, Vishnu; Anas, Muhammed; Sajeev, Sayu; Sunil, K. S.

doi:10.1007/978-981-16-3728-5_57

“…Even though there are different languages with pre-built packages to execute NLP methods, Python, Java and R programming languages are widely being used [166]. The factors to be considered in adopting a programming language includes the expertise in the specific programming language, the number of li- braries or package tools that could assist in performing NLP tasks [167]. Python [168], with its versatility and simple consistent syntax mirroring human language, can offer a huge number of NLP packages for topic modelling, word embeddings, document classification, sentiment annotations.…”

Section: Discussionmentioning

confidence: 99%

A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis

Shaik,

Tao,

Li

et al. 2023

Preprint

0

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Artificial Intelligence (AI) is a fast-growing area of study that stretching its presence to many business and research domains. Machine learning, deep learning, and natural language processing (NLP) are subsets of AI to tackle different areas of data processing and modelling. This review article presents an overview of AI's impact on education outlining with current opportunities. In the education domain, student feedback data is crucial to uncover the merits and demerits of existing services provided to students. AI can assist in identifying the areas of improvement in educational infrastructure, learning management systems, teaching practices and study environment. NLP techniques play a vital role in analyzing student feedback in textual format. This research focuses on existing NLP methodologies and applications that could be adapted to educational domain applications like sentiment annotations, entity annotations, text summarization, and topic modelling. Trends and challenges in adopting NLP in education were reviewed and explored. Contextbased challenges in NLP like sarcasm, domain-specific language, ambiguity, and aspect-based sentiment analysis are explained with existing methodologies to overcome them. Research community approaches to extract the semantic meaning of emoticons and special characters in feedback which conveys user opinion and challenges in adopting NLP in education are explored.

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“…Even though there are different languages with pre-built packages to execute NLP methods, Python, Java and R programming languages are widely being used [166]. The factors to be considered in adopting a programming language includes the expertise in the specific programming language, the number of libraries or package tools that could assist in performing NLP tasks [167]. Python [168], with its versatility and simple consistent syntax mirroring human language, can offer a huge number of NLP packages for topic modelling, word embeddings, document classification, sentiment annotations.…”

Section: Discussionmentioning

confidence: 99%

A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis

Shaik

¹

,

Tao

²

,

Li

³

et al. 2022

View full text Add to dashboard Cite

Artificial Intelligence (AI) is a fast-growing area of study that stretching its presence to many business and research domains. Machine learning, deep learning, and natural language processing (NLP) are subsets of AI to tackle different areas of data processing and modelling. This review article presents an overview of AI's impact on education outlining with current opportunities. In the education domain, student feedback data is crucial to uncover the merits and demerits of existing services provided to students. AI can assist in identifying the areas of improvement in educational infrastructure, learning management systems, teaching practices and study environment. NLP techniques play a vital role in analyzing student feedback in textual format. This research focuses on existing NLP methodologies and applications that could be adapted to educational domain applications like sentiment annotations, entity annotations, text summarization, and topic modelling. Trends and challenges in adopting NLP in education were reviewed and explored. Contextbased challenges in NLP like sarcasm, domain-specific language, ambiguity, and aspect-based sentiment analysis are explained with existing methodologies to overcome them. Research community approaches to extract the semantic meaning of emoticons and special characters in feedback which conveys user opinion and challenges in adopting NLP in education are explored.

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“…The proposed environment focuses on the syntactical side of the entered sentences in the hope of recognizing the meaning and translating it into a runnable code. Thomas et al [31] stressed the importance of handling the ambiguity issue when the natural language is processed to preserve the meaning of the sentences that are converted into precise programming language statements. Although the proposed applications assist users in writing source code with no programming knowledge, they make only a minor contribution to the development of critical thinking skills.…”

Section: Literature Reviewmentioning

confidence: 99%

Bridging the Gap: Template-Based Coding for Transitioning from Visual Logic to Text-Based Programming

Al-Imamy

2024

IJIET

0

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Visual programming environments, which utilize visual blocks or flowcharts to represent programming logic, have emerged as a key strategy to assist novice learners in overcoming the complexities associated with text-based programming. However, transitioning from these visual representations to full-text programming often presents a significant challenge for these learners. One solution to this challenge is the use of template-based coding, which has been shown to increase performance and reduce the number of errors made by students studying computer programming. This study used an experimental assignment and survey to evaluate the Code-By-Template (CBT) application among 82 students, revealing enhanced coding proficiency and positive student attitudes, highlighting the effectiveness of CBT in programming learning. The CBT, in particular, has been found to enhance students’ performance in successfully solving programming problems. The observed improvement in performance can be attributed to a 17% increase in scores (correctness) during the same time frame, suggesting a decrease in the number of errors. It is important to acknowledge that the scores mostly depend on the number and type of errors. Beyond this performance improvement, students have also expressed interest, describing the CBT as “extremely helpful” and “making programming easier.” This highlights the qualitative benefits of the tool in promoting appreciation for it, indicating its potential to enhance engagement as well as learning outcomes. The difficulty of learning programming code has had a persistent impact on retention rates in computer programming courses. However, the improved performance and learning ease facilitated by the CBT environment may offer a solution to this retention problem. By making the learning process more manageable and less error-prone, the CBT environment can help to ensure confidence-boosting and interest-fostering that lead to more students successfully completing their programming courses and continuing their studies in this field.

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Programming with Natural Languages: A Survey

Cited by 4 publications

References 9 publications

A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis

A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis

A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis

Bridging the Gap: Template-Based Coding for Transitioning from Visual Logic to Text-Based Programming

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