scholarly journals Lessons Learned from Teaching Machine Learning and Natural Language Processing to High School Students

2020 ◽  
Vol 34 (09) ◽  
pp. 13397-13403
Author(s):  
Narges Norouzi ◽  
Snigdha Chaturvedi ◽  
Matthew Rutledge
Keyword(s):  
Machine Learning ◽  
High School ◽  
High School Students ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Teaching Machine ◽  
Lessons Learned ◽  
School Students ◽  
Gender Discrepancies

This paper describes an experience in teaching Machine Learning (ML) and Natural Language Processing (NLP) to a group of high school students over an intense one-month period. In this work, we provide an outline of an AI course curriculum we designed for high school students and then evaluate its effectiveness by analyzing student's feedback and student outcomes. After closely observing students, evaluating their responses to our surveys, and analyzing their contribution to the course project, we identified some possible impediments in teaching AI to high school students and propose some measures to avoid them. These measures include employing a combination of objectivist and constructivist pedagogies, reviewing/introducing basic programming concepts at the beginning of the course, and addressing gender discrepancies throughout the course.

Maximizing ANLP Evaluation

2012 ◽  
pp. 438-456
Author(s):  
Adam Renner ◽  
Philip M. McCarthy ◽  
Chutima Boonthum-Denecke ◽  
Danielle S. McNamara
Keyword(s):  
High School ◽  
High School Students ◽  
Natural Language Processing ◽  
Language Processing ◽  
Learning Systems ◽  
School Students ◽  
Computational Approaches ◽  
Tutoring Systems ◽  
User Input ◽  
Controlled Natural Language

A continuing problem for ANLP (compared with NLP) is that language tends to be more natural in ANLP than that examined in more controlled natural language processing (NLP) studies. Specifically, ineffective or misleading feedback can result from faulty assessment of misspelled words. This chapter describes the Harmonizer system for addressing the problem of user input irregularities (e.g., typos). The Harmonizer is specifically designed for Intelligence Tutoring Systems (ITSs) that use NLP to provide assessment and feedback based on the typed input of the user. Our approach is to “harmonize” similar words to the same form in the benchmark, rather than correcting them to dictionary entries. This chapter describes the Harmonizer, and evaluates its performance using various computational approaches on unedited input from high school students in the context of an ITS (i.e., iSTART). Our results indicate that various metric approaches to NLP (such as word-overlap cohesion scores) are moderately affected when student errors are filtered by the Harmonizer. Given the prevalence of typing errors in the sample, the study substantiates the need to “clean” typed input in comparable NLP-based learning systems. The Harmonizer provides such ability and is easy to implement with light processing requirements.

scholarly journals Learning by Enhancing Half-Baked AI Projects

2021 ◽  
Author(s):  
Ken Kahn ◽  
Niall Winters
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Image Recognition ◽  
Language Processing ◽  
Speech Synthesis ◽  
Web Browsers ◽  
School Students ◽  
Hands On

AbstractWe have developed thirty sample artificial intelligence (AI) programs in a form suitable for enhancement by non-expert programmers. The projects are implemented in the Snap! blocks language and can be run in modern web browsers. These projects have been designed to be modifiable by school students and have been iteratively developed with over 100 students. The projects involve speech synthesis, speech and image recognition, natural language processing, and deep machine learning. They illustrate a variety of AI capabilities, concepts, and techniques. The intent is to provide students with hands-on experience with AI programming so they come to understand the possibilities, problems, strengths, and weaknesses of AI today.

How Much Knowledge Is Too Little? When a Lack of Knowledge Becomes a Barrier to Comprehension

2019 ◽  
Vol 30 (9) ◽  
pp. 1344-1351 ◽  
Author(s):  
Tenaha O’Reilly ◽  
Zuowei Wang ◽  
John Sabatini
Keyword(s):  
High School ◽  
High School Students ◽  
Natural Language Processing ◽  
Language Processing ◽  
Positive Relation ◽  
Specific Topic ◽  
School Students ◽  
Knowledge Test ◽  
Processing Techniques ◽  
The Relationship

Have you ever found it difficult to read something because you lack knowledge on the topic? We investigated this phenomenon with a sample of 3,534 high school students who took a background-knowledge test before working on a reading-comprehension test on the topic of ecology. Broken-line regression revealed a knowledge threshold: Below the threshold, the relationship between comprehension and knowledge was weak (β = 0.18), but above the threshold, a strong and positive relation emerged (β = 0.81). Further analyses indicated that certain topically relevant words (e.g., ecosystem, habitat) were more important to know than others when predicting the threshold, and these keywords could be identified using natural-language-processing techniques. Collectively, these results may help identify who is likely to have a problem comprehending information on a specific topic and, to some extent, what knowledge is likely required to comprehend information on that topic.

A Machine Learning Application for Raising WASH Awareness in the Times of COVID-19 Pandemic (Preprint)

2020 ◽  
Author(s):  
Rohan Pandey ◽  
Vaibhav Gautam ◽  
Ridam Pal ◽  
Harsh Bandhey ◽  
Lovedeep Singh Dhingra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
User Feedback ◽  
Who Guidelines ◽  
The Times ◽  
The Right ◽  
Local Languages

BACKGROUND The COVID-19 pandemic has uncovered the potential of digital misinformation in shaping the health of nations. The deluge of unverified information that spreads faster than the epidemic itself is an unprecedented phenomenon that has put millions of lives in danger. Mitigating this ‘Infodemic’ requires strong health messaging systems that are engaging, vernacular, scalable, effective and continuously learn the new patterns of misinformation. OBJECTIVE We created WashKaro, a multi-pronged intervention for mitigating misinformation through conversational AI, machine translation and natural language processing. WashKaro provides the right information matched against WHO guidelines through AI, and delivers it in the right format in local languages. METHODS We theorize (i) an NLP based AI engine that could continuously incorporate user feedback to improve relevance of information, (ii) bite sized audio in the local language to improve penetrance in a country with skewed gender literacy ratios, and (iii) conversational but interactive AI engagement with users towards an increased health awareness in the community. RESULTS A total of 5026 people who downloaded the app during the study window, among those 1545 were active users. Our study shows that 3.4 times more females engaged with the App in Hindi as compared to males, the relevance of AI-filtered news content doubled within 45 days of continuous machine learning, and the prudence of integrated AI chatbot “Satya” increased thus proving the usefulness of an mHealth platform to mitigate health misinformation. CONCLUSIONS We conclude that a multi-pronged machine learning application delivering vernacular bite-sized audios and conversational AI is an effective approach to mitigate health misinformation. CLINICALTRIAL Not Applicable

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