The effects of using corpora on revision tasks in L2 writing with coded error feedback

ReCALL ◽  
2014 ◽  
Vol 26 (2) ◽  
pp. 147-162 ◽  
Author(s):  
Yukio Tono ◽  
Yoshiho Satake ◽  
Aika Miura
Keyword(s):  
Undergraduate Students ◽  
Language Processing ◽  
Error Feedback ◽  
Part Of Speech ◽  
Significant Difference ◽  
Hands On ◽  
Query Tool ◽  
Corpus Data ◽  
Short Essay ◽  
Processing Techniques

AbstractThis study reports on the results of classroom research investigating the effects of corpus use in the process of revising compositions in English as a foreign language. Our primary aim was to investigate the relationship between the information extracted from corpus data and how that information actually helped in revising different types of errors in the essays. In ‘data-driven learning’, previous research has often failed to provide rigorous criteria for choosing the words or phrases suitable for correction with corpus data. By investigating the above relationship, this study aims to clarify what should be corrected by looking at corpus data. 93 undergraduate students from two universities in Tokyo wrote a short essay in 20 minutes without a dictionary, and the instructors gave coded error feedback for two lexical or grammatical errors. They deliberately selected one error which should be appropriate for checking against corpus data and one that was more likely to be corrected without using any reference resource. Three weeks later, a short hands-on instruction of the corpus query tool was given, followed by revision activities in which the participants were instructed to revise their first drafts, with or without the tool depending on the codes given to each error. 188 errors were automatically classified into three different categories (omission, addition and misformation) using natural language processing techniques. All words and phrases tagged for errors were further annotated for part-of-speech (POS) information. The results show that there was a significant difference in the accuracy rate among the three error types when the students consulted the corpus: omission and addition errors were easily identified and corrected, whereas misformation errors were low in correction accuracy. This reveals that certain errors are more suitable for checking against corpus data than others.

scholarly journals Sentiment Analysis on Twitter Data of World Cup Soccer Tournament Using Machine Learning

IoT ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 218-239 ◽  
Author(s):  
Ravikumar Patel ◽  
Kalpdrum Passi
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Random Forest ◽  
Natural Language ◽  
Language Processing ◽  
Machine Learning Algorithms ◽  
World Cup ◽  
Part Of Speech ◽  
Twitter Data ◽  
Processing Techniques

In the derived approach, an analysis is performed on Twitter data for World Cup soccer 2014 held in Brazil to detect the sentiment of the people throughout the world using machine learning techniques. By filtering and analyzing the data using natural language processing techniques, sentiment polarity was calculated based on the emotion words detected in the user tweets. The dataset is normalized to be used by machine learning algorithms and prepared using natural language processing techniques like word tokenization, stemming and lemmatization, part-of-speech (POS) tagger, name entity recognition (NER), and parser to extract emotions for the textual data from each tweet. This approach is implemented using Python programming language and Natural Language Toolkit (NLTK). A derived algorithm extracts emotional words using WordNet with its POS (part-of-speech) for the word in a sentence that has a meaning in the current context, and is assigned sentiment polarity using the SentiWordNet dictionary or using a lexicon-based method. The resultant polarity assigned is further analyzed using naïve Bayes, support vector machine (SVM), K-nearest neighbor (KNN), and random forest machine learning algorithms and visualized on the Weka platform. Naïve Bayes gives the best accuracy of 88.17% whereas random forest gives the best area under the receiver operating characteristics curve (AUC) of 0.97.

scholarly journals Criterias for the selection of rabbits suitable for animal-assisted work with the visually impaired (preliminary study)

2021 ◽  
Vol 25 (1) ◽  
pp. 3-15
Author(s):  
Réka Iváncsik ◽  
Marcell Molnár
Keyword(s):  
Undergraduate Students ◽  
Visually Impaired ◽  
Animal Species ◽  
Random Order ◽  
Care Needs ◽  
Visually Impaired People ◽  
Significant Difference ◽  
Hands On ◽  
Preliminary Study ◽  
Selection Of

Animal assisted interventions in everyday life can help reduce stress and make life of the visually impaired more complete. For this, not only dogs are available, but also other animal species. We chose the dwarf rabbit for this purpose. The dwarf rabbit is a popular pet, soft, confidential, hands-on; can be taught basic rules, and its care needs are easier to meet for a visually impaired than a dog's. The objective of our research was to develop and test a set of criteria for the selection of rabbits suitable for the visually impaired, furthermore, to determine whether a person with sight is able to select rabbits for the visually impaired, or whether there are large differences in their assessment? In the course of research, we developed a 14-point criteria that included confidential questions, pleasant experience questions and questions about the stress of rabbits. The scoring scale ranged from 1 to 5, with the highest point marking the most suitable rabbit. The rabbits in the study were of 6 to 12 months of age, tamed for four generations, of different sizes, hair lengths and colours. The study included 12 special education undergraduate students and one person with visual impairment. The participants worked in pairs, first blindfolded and then with sight of the rabbits. The rabbits were assigned in random order, so students didn't know what number of point the rabbits had previously received. The eye-binding of the students did not affect the scoring, but the visually impaired subject gave the rabbits an average of 0.1 points higher. Because the scores for each student were high, we did not get a significant result. We looked at who at what chance could have given each points. It turned out that the visually impaired gave 5 points - 10% of the time more often - and gave 3 points - 3% - than the undergrad students. We looked at which of the 14 aspects had greater differences in their perception: there were differences, but they were not significant. Comparing the rabbits, we received a significant difference, based on which this criteria system may be useable for the selection of rabbits suitable for visually impaired, as significant differences were discovered between rabbits. People with sight can also use the test, but they slightly more rigorously. It is recommended to conduct further studies involving several visually impaired people.

Automatic Extraction of Causally Related Functions From Natural-Language Text for Biomimetic Design

2012 ◽  
Author(s):  
Hyunmin Cheong ◽  
L. H. Shu
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Biological Information ◽  
Research Approach ◽  
Initial Development ◽  
Biomimetic Design ◽  
Natural Language Text ◽  
Part Of Speech ◽  
Extraction Algorithm ◽  
Processing Techniques

Identifying relevant analogies from biology is a significant challenge in biomimetic design. Our natural-language approach addresses this challenge by developing techniques to search biological information in natural-language format, such as books or papers. This paper presents the application of natural-language processing techniques, such as part-of-speech tags, typed-dependency parsing, and syntactic patterns, to automatically extract and categorize causally related functions from text with biological information. Causally related functions, which specify how one action is enabled by another action, are considered important for both knowledge representation used to model biological information and analogical transfer of biological information performed by designers. An extraction algorithm was developed and scored F-measures of 0.78–0.85 in an initial development test. Because this research approach uses inexpensive and domain-independent techniques, the extraction algorithm has the potential to automatically identify patterns of causally related functions from a large amount of text that contains either biological or design information.

scholarly journals Identification of essential attributes in conclusions of student reports

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Aurelio López-López ◽  
Samuel González-López ◽  
Jesús Miguel García-Gorrostieta
Keyword(s):  
Undergraduate Students ◽  
Language Processing ◽  
Control Group ◽  
Pilot Test ◽  
Value Judgments ◽  
Processing Techniques ◽  
Student Reports ◽  
Future Work ◽  
Experimental Group ◽  
Statistical Results

Abstract This work seeks to help students in improving their first research reports, based on natural language processing techniques. We present a Conclusion model that includes three schemes: Goal Connectedness, Judgment and Speculation. These subsystems try to account for the main expected attributes in conclusions, specifically the Connectedness with the general objective of the research, the evidence of value Judgments, and the presence of Future work as a result of the student reflection after the inquiry. The article details the schemes, a validation of the approach in an annotated corpus, and a pilot test with undergraduate students. Results of a prior validation indicate that student writings indeed adhere to such attributes, especially at graduate level. Statistical results of the pilot test showed that undergraduate students in an experimental group achieved improved conclusion content when compared with the control group.

Integrating Disciplines in a Plant Chemistry Laboratory Module to Enhance Interdisciplinary and Scientific Thinking in Undergraduate Students

2019 ◽  
Author(s):  
Lucas Busta ◽  
Sabrina E. Russo
Keyword(s):  
Critical Thinking ◽  
Medicinal Plant ◽  
Systems Thinking ◽  
Undergraduate Students ◽  
Core Competencies ◽  
Chemistry Laboratory ◽  
Scientific Thinking ◽  
Written Communication ◽  
Plant Chemistry ◽  
Hands On

Here, we describe a hands-on medicinal plant chemistry laboratory module (Phytochemical Laboratory Activities for iNtegrative Thinking and Enhanced Competencies; PLANTEC) for undergraduates that targets the development of core competencies in (i) critical thinking and analysis of text and data, (ii) interdisciplinary and systems thinking, (iii) oral and written communication of science, and (iv) teamwork and collaboration.<br>

An Automatic Question Generation System using Rule-Based Approach in Bloom’s Taxonomy

2019 ◽  
Vol 13 ◽  
Author(s):  
G Deena ◽  
K Raja ◽  
K Kannan
Keyword(s):  
Language Processing ◽  
Learning Process ◽  
Question Generation ◽  
Test Question ◽  
Rule Based ◽  
Part Of Speech ◽  
Core Idea ◽  
Rule Based Approach ◽  
Teaching Learning ◽  
Automatic Question Generation

: In this competing world, education has become part of everyday life. The process of imparting the knowledge to the learner through education is the core idea in the Teaching-Learning Process (TLP). An assessment is one way to identify the learner’s weak spot of the area under discussion. An assessment question has higher preferences in judging the learner's skill. In manual preparation, the questions are not assured in excellence and fairness to assess the learner’s cognitive skill. Question generation is the most important part of the teaching-learning process. It is clearly understood that generating the test question is the toughest part. Methods: Proposed an Automatic Question Generation (AQG) system which automatically generates the assessment questions dynamically from the input file. Objective: The Proposed system is to generate the test questions that are mapped with blooms taxonomy to determine the learner’s cognitive level. The cloze type questions are generated using the tag part-of-speech and random function. Rule-based approaches and Natural Language Processing (NLP) techniques are implemented to generate the procedural question of the lowest blooms cognitive levels. Analysis: The outputs are dynamic in nature to create a different set of questions at each execution. Here, input paragraph is selected from computer science domain and their output efficiency are measured using the precision and recall.

Effects of Audience Response Systems on Student Achievement and Long-Term Retention

2011 ◽  
Vol 39 (10) ◽  
pp. 1431-1439 ◽  
Author(s):  
Selcuk Karaman
Keyword(s):  
Academic Success ◽  
Treatment Group ◽  
Undergraduate Students ◽  
Course Design ◽  
Control Group ◽  
Audience Response ◽  
Multiple Choice Questions ◽  
Audience Response Systems ◽  
Significant Difference ◽  
Response Systems

The effects of audience response systems (ARS) on students' academic success and their perceptions of ARS were examined in this study. Participants, comprising 44 undergraduate students, were randomly assigned to a control or treatment group. The course design was the same for both groups and the instructor prepared the multiple-choice questions in advance; students in the control group responded to these questions verbally whereas the treatment group used ARS. Two paper-based examinations were used to measure the learning of concepts and skills that were taught. Students' perceptions of ARS were collected via a questionnaire. Results showed that ARS usage has a significant learning achievement effect in the first 4 weeks but not at the end of the second 4 weeks. There was no significant difference in retention between either group. Students perceived the ARS tool positively, finding it very enjoyable and useful.

Musical pantophagy: Is there an effect on changes in preference resulting from repeated exposure?

2021 ◽  
pp. 030573562098729
Author(s):  
Rebecca R Johnston ◽  
Gina M Childers
Keyword(s):  
Undergraduate Students ◽  
Classical Music ◽  
Effective Means ◽  
Affective Response ◽  
Repeated Exposure ◽  
Positive Trend ◽  
Preference Rating ◽  
Significant Difference ◽  
Music Consumption ◽  
Post Test

The purpose of this research was to examine the effects of musical pantophagy, classical music consumption, and initial receptivity to select musical examples on changes in preference rating resulting from a program of repeated exposure. Participants included undergraduate students enrolled in a section of music appreciation at a large Southeastern university ( n = 67). Data were collected using a research designed preference rating measure (PRM) administered during a 5-week period within which there were eight test measures. Participants were divided into quartiles. Pre- to post-test measures resulted in a general positive trend for all participants. Comparisons of Q1 (lowest pantophagy) and Q3 (highest pantophagy) on PRMs 1–8 yielded no differences between groups, and PRM 8 was significantly different from PRM 1 for both groups. The same comparisons for Q1 (non-Classical music consumption) indicated significant difference with large effect size and for Q1 (lowest initial receptivity) indicated significant difference. Results suggest that regardless of musical pantophagy, repetition is an effective means by which to increase affective response to music, and that students who do not currently consume formal art music and who have low initial receptivity may report greater increases in affective response to music over time.

scholarly journals Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Naïve Bayes

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 204
Author(s):  
Charlyn Villavicencio ◽  
Julio Jerison Macrohon ◽  
X. Alphonse Inbaraj ◽  
Jyh-Horng Jeng ◽  
Jer-Guang Hsieh
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Data Science ◽  
Naive Bayes ◽  
The Philippines ◽  
Naïve Bayes ◽  
Social Networking Site ◽  
Bayes Model ◽  
The Government ◽  
Processing Techniques

A year into the COVID-19 pandemic and one of the longest recorded lockdowns in the world, the Philippines received its first delivery of COVID-19 vaccines on 1 March 2021 through WHO’s COVAX initiative. A month into inoculation of all frontline health professionals and other priority groups, the authors of this study gathered data on the sentiment of Filipinos regarding the Philippine government’s efforts using the social networking site Twitter. Natural language processing techniques were applied to understand the general sentiment, which can help the government in analyzing their response. The sentiments were annotated and trained using the Naïve Bayes model to classify English and Filipino language tweets into positive, neutral, and negative polarities through the RapidMiner data science software. The results yielded an 81.77% accuracy, which outweighs the accuracy of recent sentiment analysis studies using Twitter data from the Philippines.

scholarly journals Shall I Work with Them? A Knowledge Graph-Based Approach for Predicting Future Research Collaborations

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 664
Author(s):  
Nikos Kanakaris ◽  
Nikolaos Giarelis ◽  
Ilias Siachos ◽  
Nikos Karacapilidis
Keyword(s):  
Language Processing ◽  
Scientific Knowledge ◽  
Link Prediction ◽  
Performance Metrics ◽  
Future Research ◽  
Knowledge Graph ◽  
Prediction Problem ◽  
Textual Information ◽  
Research Collaborations ◽  
Processing Techniques

We consider the prediction of future research collaborations as a link prediction problem applied on a scientific knowledge graph. To the best of our knowledge, this is the first work on the prediction of future research collaborations that combines structural and textual information of a scientific knowledge graph through a purposeful integration of graph algorithms and natural language processing techniques. Our work: (i) investigates whether the integration of unstructured textual data into a single knowledge graph affects the performance of a link prediction model, (ii) studies the effect of previously proposed graph kernels based approaches on the performance of an ML model, as far as the link prediction problem is concerned, and (iii) proposes a three-phase pipeline that enables the exploitation of structural and textual information, as well as of pre-trained word embeddings. We benchmark the proposed approach against classical link prediction algorithms using accuracy, recall, and precision as our performance metrics. Finally, we empirically test our approach through various feature combinations with respect to the link prediction problem. Our experimentations with the new COVID-19 Open Research Dataset demonstrate a significant improvement of the abovementioned performance metrics in the prediction of future research collaborations.

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