End-to-End generation of Multiple-Choice questions using Text-to-Text transfer Transformer models

Rodriguez-Torrealba, Ricardo; García-López, Eva; García-Cabot, Antonio

doi:10.1016/j.eswa.2022.118258

Cited by 43 publications

(29 citation statements)

References 23 publications

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“…Older papers are excluded from the search criteria. Most of them used automated evaluation tools [4][5] [6] [14] [17] or they used automated contexts for programming languages [20] [21] [15]. It was also identified that generating feedback for the questions produced to teach programming languages are equally important.…”

Section: Research Analysis and Discussionmentioning

confidence: 99%

“…For example, [13] used programming-by-example for code snippets that was a test regulated by synthesis. Recently, the research article [15] provided facts for generating questions for teaching programming languages. The study reported in [16] found that students are taught by teachers to produce a program but they are not fully sure about the use of their own codes as they failed to grasp basic concepts.…”

Section: B Automatic Question Generation In Teaching Programmingmentioning

confidence: 99%

“…These are the following: 1) after COVID-19, the instructors valued code-writing question generation techniques like Junit that is a web-based tool and used user feedback as a validating tool. 2) Next, ITA [12], JAVA software, C programming for generating www.ijacsa.thesai.org questions, CodeTraining [15], dynamic codes [16] and PQG model [23] were employed by instructors for generating questions that were identified as the key source of positive ratings in the assessment of students during exams. 3) The analysis of articles published after COVID-19, i.e.…”

Section: A Comparison Of State-of-the-field Before Covid-19 and After...mentioning

confidence: 99%

“…In order to address this limitation, the research in [19] used another technique, called basic static analysis and identified the misunderstood concepts and the inadequacy of code structures. However, recently the article [15] also concluded that Program Transformations are another language processing tool that reduces the syntactical complications and help instructors to produce same level of abstraction when designing questions. Notably, all the tools identified during the analysis of articles showed that they fall within two major categories; automated assessment (AA) and intelligent tutoring system (ITS).…”

Section: A Comparison Of State-of-the-field Before Covid-19 and After...mentioning

confidence: 99%

“…A recent study conducted by [15] mentioned that previous meta-analysis methods have been used to recognize the factors affecting students' performance but this study is pioneered in evaluating students' computer programming skills through Educational Data mining approaches. The authors stated that there are various categorizations of algorithms for identifying student's performance studying computer programming and the most effective is to use pooled approach to estimate students' performance progress in programming as their educational domain.…”

Section: A Comparison Of State-of-the-field Before Covid-19 and After...mentioning

confidence: 99%

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A Review of Automatic Question Generation in Teaching Programming

Alshboul¹,

Baksa-Varga²

2022

IJACSA

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Computer programming is a complex field that requires rigorous practice in programming code writing and learning skills, which can be one of the critical challenges in learning and teaching programming. The complicated nature of computer programming requires an instructor to manage its learning resources and diligently generate programming-related questions for students that need conceptual programming and procedural programming rules. In this regard, automatic question generation techniques help teachers carefully align their learning objectives with the question designs in terms of relevancy and complexity. This also helps in reducing the costs linked with the manual generation of questions and fulfills the need of supplying new questions through automatic question techniques. This paper presents a theoretical review of automatic question generation (AQG) techniques, particularly related to computer programming languages from the year 2017 till 2022. A total of 18 papers are included in this study. one of the goals is to analyze and compare the state of the field in question generation before COVID-19 and after the COVID-19 period, and to summarize the challenges and future directions in the field. In congruence to previous studies, there is little focus given in the existing literature on generating questions related to learning programming languages through different techniques. Our findings show that there is a need to further enhance experimental studies in implementing automatic question generation especially in the field of programming. Also, there is a need to implement an authoring tool that can demonstrate designing more practical evaluation metrics for students.

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Section: Research Analysis and Discussionmentioning

confidence: 99%

Section: B Automatic Question Generation In Teaching Programmingmentioning

confidence: 99%

Section: A Comparison Of State-of-the-field Before Covid-19 and After...mentioning

confidence: 99%

Section: A Comparison Of State-of-the-field Before Covid-19 and After...mentioning

confidence: 99%

Section: A Comparison Of State-of-the-field Before Covid-19 and After...mentioning

confidence: 99%

See 3 more Smart Citations

A Review of Automatic Question Generation in Teaching Programming

Alshboul¹,

Baksa-Varga²

2022

IJACSA

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Exploring the impact of language models, such as ChatGPT, on student learning and assessment

Zirar

2023

Review of Education

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Recent developments in language models, such as ChatGPT, have sparked debate. These tools can help, for example, dyslexic people, to write formal emails from a prompt and can be used by students to generate assessed work. Proponents argue that language models enhance the student experience and academic achievement. Those concerned argue that language models impede student learning and call for a cautious approach to their adoption. This paper aims to provide insights into the role of language models in reshaping student learning and assessment in higher education. For that purpose, it probes the impact of language models, specifically ChatGPT, on student learning and assessment. It also explores the implications of language models in higher education settings, focusing on their effects on pedagogy and evaluation. Using the Scopus database, a search protocol was employed to identify 25 articles based on relevant keywords and selection criteria. The developed themes suggest that language models may alter how students learn and are assessed. While language models can provide information for problem‐solving and critical thinking, reliance on them without critical evaluation adversely impacts student learning. Language models can also generate teaching and assessment material and evaluate student responses, but their role should be limited to ‘play a specific and defined role’. Integration of language models in student learning and assessment is only helpful if students and educators play an active and effective role in checking the generated material's validity, reliability and accuracy. Propositions and potential research questions are included to encourage future research.

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Transformer technology in molecular science

Jiang,

Ke,

Chen

et al. 2024

WIREs Comput Mol Sci

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A transformer is the foundational architecture behind large language models designed to handle sequential data by using mechanisms of self‐attention to weigh the importance of different elements, enabling efficient processing and understanding of complex patterns. Recently, transformer‐based models have become some of the most popular and powerful deep learning (DL) algorithms in molecular science, owing to their distinctive architectural characteristics and proficiency in handling intricate data. These models leverage the capacity of transformer architectures to capture complex hierarchical dependencies within sequential data. As the applications of transformers in molecular science are very widespread, in this review, we only focus on the technical aspects of transformer technology in molecule domain. Specifically, we will provide an in‐depth investigation into the algorithms of transformer‐based machine learning techniques in molecular science. The models under consideration include generative pre‐trained transformer (GPT), bidirectional and auto‐regressive transformers (BART), bidirectional encoder representations from transformers (BERT), graph transformer, transformer‐XL, text‐to‐text transfer transformer, vision transformers (ViT), detection transformer (DETR), conformer, contrastive language‐image pre‐training (CLIP), sparse transformers, and mobile and efficient transformers. By examining the inner workings of these models, we aim to elucidate how their architectural innovations contribute to their effectiveness in processing complex molecular data. We will also discuss promising trends in transformer models within the context of molecular science, emphasizing their technical capabilities and potential for interdisciplinary research. This review seeks to provide a comprehensive understanding of the transformer‐based machine learning techniques that are driving advancements in molecular science.This article is categorized under: Data Science > Chemoinformatics Data Science > Artificial Intelligence/Machine Learning

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End-to-End generation of Multiple-Choice questions using Text-to-Text transfer Transformer models

Cited by 43 publications

References 23 publications

A Review of Automatic Question Generation in Teaching Programming

A Review of Automatic Question Generation in Teaching Programming

Exploring the impact of language models, such as ChatGPT, on student learning and assessment

Transformer technology in molecular science

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