Beat the Books Gamifying the Learning Experience for K-6 Students Using Leitner System
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A Queueing-Theoretic Foundation for Optimal Spaced Repetition
A Queueing-Theoretic Foundation for Optimal Spaced Repetition Siddharth Reddy [email protected] Department of Computer Science, Cornell University, Ithaca, NY 14850 Igor Labutov [email protected] Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14850 Siddhartha Banerjee [email protected] School of Operations Research and Information Engineering, Cornell University, Ithaca, NY 14850 Thorsten Joachims [email protected] Department of Computer Science, Cornell University, Ithaca, NY 14850 1. Extended Abstract way back to 1885 and the pioneering work of Ebbinghaus (Ebbinghaus, 1913), identify two critical variables that de- In the study of human learning, there is broad evidence that termine the probability of recalling an item: reinforcement, our ability to retain a piece of information improves with i.e., repeated exposure to the item, and delay, i.e., time repeated exposure, and that it decays with delay since the since the item was last reviewed. Accordingly, scientists last exposure. This plays a crucial role in the design of ed- have long been proponents of the spacing effect for learn- ucational software, leading to a trade-off between teaching ing: the phenomenon in which periodic, spaced review of new material and reviewing what has already been taught. content improves long-term retention. A common way to balance this trade-off is spaced repe- tition, which uses periodic review of content to improve A significant development in recent years has been a grow- long-term retention. Though spaced repetition is widely ing body of work that attempts to ‘engineer’ the process used in practice, e.g., in electronic flashcard software, there of human learning, creating tools that enhance the learning is little formal understanding of the design of these sys- process by building on the scientific understanding of hu- tems. -
Variational Deep Knowledge Tracing for Language Learning
Variational Deep Knowledge Tracing for Language Learning Sherry Ruan Wei Wei James A. Landay [email protected] [email protected] [email protected] Stanford University Google Stanford University Stanford, California, United States Sunnyvale, California, United States Stanford, California, United States ABSTRACT You are✔ buying shoes Deep Knowledge Tracing (DKT), which traces a student’s knowl- edge change using deep recurrent neural networks, is widely adopted in student cognitive modeling. Current DKT models only predict You are✘ young Yes you are✘ going a student’s performance based on the observed learning history. However, a student’s learning processes often contain latent events not directly observable in the learning history, such as partial under- You are✔ You are✔ You are✔ You are✔ standing, making slips, and guessing answers. Current DKT models not going not real eating cheese welcome fail to model this kind of stochasticity in the learning process. To address this issue, we propose Variational Deep Knowledge Tracing (VDKT), a latent variable DKT model that incorporates stochasticity Linking verb Tense ✔/✘ Right/Wrong Attempt into DKT through latent variables. We show that VDKT outper- forms both a sequence-to-sequence DKT baseline and previous Figure 1: A probabilistic knowledge tracing system tracks SoTA methods on MAE, F1, and AUC by evaluating our approach the distribution of two concepts concerning the word are: on two Duolingo language learning datasets. We also draw various understanding it as a linking verb and understanding it in interpretable analyses from VDKT and offer insights into students’ the present progressive tense. 3 and 7 represent whether stochastic behaviors in language learning. -
VAASAN AMMATTIKORKEAKOULU VAASA UNIVERSITY of APPLIED SCIENCES Degree Programme in Information Technology
Klim Skuridin SPACED REPETITION CLOUD WEBAPP IN JAVA EE AND GOOGLE MATERIAL DESIGN Information Technology 2016 VAASAN AMMATTIKORKEAKOULU VAASA UNIVERSITY OF APPLIED SCIENCES Degree Programme in Information Technology ABSTRACT Author Klim Skuridin Title Spaced Repetition Cloud WebApp in Java EE and Google Material Design Year 2016 Language English Pages 42 Name of Supervisor Pirjo Prosi The aim of this thesis project was to develop a web application using Java with deployment on Amazon Web Services cloud platform within an Elastic Computing 2 VM instance. The developer aims to help users learn new information using the Spaced Repetition principle. The main tools of the project include Java/JSP for back-end development, Twitter Bootstrap framework for front-end development, GitHub Version Control System for code management, MySQL for app data per- sistence, Tomcat server for deployment and Eclipse IDE for coding the back-end. Keywords: Java, WebApp, AWS, Spaced Repetition, Bootstrap, GMD 3(42) CONTENTS 1. INTRODUCTION ............................................................................................ 6 2. PROJECT BACKGROUND & DESCRIPTION ............................................. 8 2.1 Project Description.................................................................................... 8 2.2 Application Algorithm .............................................................................. 9 2.3 Relevant Technologies ............................................................................ 11 3. ANALYSIS & DESIGN ................................................................................ -
Celebrating 40 Years of TLT Feature Article My Share
The Language Teacher http://jalt-publications.org/tlt Celebrating 40 years of TLT Feature Article My Share An Edited Version of the First Eight Classroom ideas from Gary Henscheid, 3 13 1,000-Word Frequency Bands of Nick Caine, Douglas Perkins and Adam the Japanese-English Version of the Pearson, and Richard Buckley Vocabulary Size Test Stuart McLean, Tomoko Ishii, Tim Stoeckel, Phil Bennett, and Yuko JALT Praxis Matsumoto TLT Wired 18 Young Learners Readers’ Forum 20 Book Reviews Brain-Friendly Learning Tips for 22 9 Teaching Assistance Long-Term Retention and Recall 26 The Writers’ Workshop Jeff Mehring and Regan Thomson 28 Dear TLT 30 SIG Focus: School Owners’ SIG 33 Old Grammarians 35 The Japan Association for Language Teaching Volume 40, Number 4 • July / August 2016 ISSN 0289-7938 • ¥1,900 • TLT uses recycled paper EASY-ENGLISH ADVENTURES WITH 8 DIFFERENT ENDINGS 2015 ELTon Nomination for Best Learner Resource 2015 Language Learner Literature Award Winner 2015 Language Learner Literature Award Finalist 2016 Language Learner Literature Award Finalist “Entertaining and educational” -LLL Awards Judge AWARD-WINNING GAMEBOOK SERIES・MADE IN JAPAN・IDEAL FOR EXTENSIVE READING ALSO SELF-STUDY・TIMED READING・LITERATURE CIRCLES・DISCUSSION TASKS・MORE! BUT DON’T JUST TAKE OUR WORD FOR IT. READ ONE NOW, FREE, ONLINE: Scan the QR code and Try it in class! There’s no catch! be reading in seconds Well, okay... it’s a limited time offer - but when it ends, all you need to do is join our newsletter, and you’ll still get access to a free online book! Newsletter: http://eepurl.com/P8z45 http://goo.gl/z8GUhS Print available from englishbooks.jp. -
The Indirect Spaced Repetition Concept Louis Lafleur Ritsumeikan University
Vocabulary Learning and Instruction Volume 9, Issue 2, August 2020 http://vli-journal.org The Indirect Spaced Repetition Concept Louis Lafleur Ritsumeikan University Abstract The main goal of this research is to systemize, build, and test prototype software to demonstrate Indirect Spaced Repetition (ISR) as a viable concept for Second Language Vocabulary Acquisition (SLVA). ISR is designed around well-founded spaced repetition and SLVA principles. Most importantly, it is based on Nation’s (2001) recommendation to consider all three tiers of word knowledge (meaning, form, and func- tion/use) and subsequent 18 aspects of word knowledge for a more bal- anced approach in teaching and learning vocabulary. ISR prototype software was achieved in the conceptual phase of the research. The re- sulting prototype flashcard software was given an in-depth trial for a period of 2 weeks by seven university students. Participants were given a post-project survey to evaluate ISR software (ISRS) under four cat- egories: enjoyment, usefulness, usability, and general consideration. Post-test survey findings showed above-average satisfaction and consid- eration to use such software in the future. However, these findings also revealed that some areas could be further improved, such as addressing some hardware/software issues (e.g., IT infrastructure problematics and lag) and integrating gamification elements (e.g., performance feedback/ reports). Keywords: Vocabulary learning, (Indirect) Spaced Repetition, (Spaced) Interleaving, 18 aspects of word knowledge, Computer Assisted Language Learning (CALL) 1 Background Spaced Repetition is often mistaken as a new concept as the term is often asso- ciated with recently published study software and applications. In many cases, these programs fail to give credit to the founders of the spaced repetition system (SRS). -
Studium: Your Personal Mobile Study On-The-Go
STUDIUM: YOUR PERSONAL MOBILE STUDY ON-THE-GO by Fariha Ahmed HBA, International Relations, Political Science and French, University of Toronto, 2018 A Major Research Project presented to Ryerson University in partial fulfillment of the requirements for the degree of Master of Digital Media in the Program of Digital Media Toronto, Ontario, Canada, 2019 ©Fariha Ahmed, 2019 Author’s Declaration for Electronic Submission of an MRP I hereby declare that I am the sole author of this MRP. This is a true copy of the MRP, including any required final revisions. I authorize Ryerson University to lend this MRP to other institutions or individuals for the purpose of scholarly research. I further authorize Ryerson University to reproduce this MRP by photocopying or by other means, in total or in part, at the request of other institutions or individuals for the purpose of scholarly research. I understand that my MRP may be made electronically available to the public. ii STUDIUM: YOUR PERSONAL MOBILE STUDY ON-THE-GO Fariha Ahmed Master of Digital Media Digital Media Ryerson University, 2019 ABSTRACT The landscape of studying is changing. There are now increasingly more mobile devices that allow people to learn content in numerous ways. This means that mobile devices play a large role in how a whole new generation of children, adolescents, teenagers and young adults understand information. Studium is a mobile application prototype that I have created to demonstrate how mobile devices can be used as a learning tool to enhance academic performance among post- secondary students. The objective of Studium is to illustrate how artificial intelligence can be incorporated into mobile learning applications to improve one’s studying by generating instant practice tests based off notes from lectures or readings. -
Numer 1(9)/2016
ISSN 2080-4555 Czasopismo ludologiczne Polskiego Towarzystwa Badania Gier Czasopismo wydawane przy współpracy z Pracownią Badań Ludologicznych Instytutu Lingwistyki Stosowanej Wydziału Neofilologii UAM Numer 1(9)/2016 E TOWA KI RZ S Y L S O T P W O B Polskie Towarzystwo Badania Gier A D A N I A G I R E Oficjalne czasopismo Polskiego Towarzystwa Badania Gier Homo Ludens Homo Ludens (ISSN 2080-4555) is the official journal of the Games Research Association of Poland (Polskie Towarzystwo Bada- nia Gier). The journal carries original articles on various aspects of ludology as broadly perceived games research in the hu- manities, social and other sciences. It presents a representative survey of empirical and theoretical research conducted in this area in Poland and abroad as well as reflections on issues in the area of game studies. It also publishes selected book reviews in this area. The language of the journal is basically Polish but articles in English and German are also accepted. The journal is issued on line in a form of a continuous publication - before publishing of the final versions of the texts on its website early citation versions are available. The articles are also available in a print version of the journal issued usually after the publica- tion of the final digital version. The original version of the journal is the digital one. Kolegium Redakcyjne / Editorial board Redaktor założyciel / Founding Editor: Augustyn Surdyk Redaktor naczelny / Editor-in-Chief: Augustyn Surdyk Zastępca redaktora naczelnego / Associate Editor: Jerzy -
A Trainable Spaced Repetition Model for Language Learning
A Trainable Spaced Repetition Model for Language Learning Burr Settles∗ Brendan Meeder† Duolingo Uber Advanced Technologies Center Pittsburgh, PA USA Pittsburgh, PA USA [email protected] [email protected] Abstract view sessions a few seconds apart, then minutes, then hours, days, months, and so on, with each We present half-life regression (HLR), a successive review stretching out over a longer and novel model for spaced repetition practice longer time interval. with applications to second language ac- The effects of spacing and lag are well- quisition. HLR combines psycholinguis- established in second language acquisition re- tic theory with modern machine learning search (Atkinson, 1972; Bloom and Shuell, 1981; techniques, indirectly estimating the “half- Cepeda et al., 2006; Pavlik Jr and Anderson, life” of a word or concept in a student’s 2008), and benefits have also been shown for gym- long-term memory. We use data from nastics, baseball pitching, video games, and many Duolingo — a popular online language other skills. See Ruth (1928), Dempster (1989), learning application — to fit HLR models, and Donovan and Radosevich (1999) for thorough reducing error by 45%+ compared to sev- meta-analyses spanning several decades. eral baselines at predicting student recall Most practical algorithms for spaced repetition rates. HLR model weights also shed light are simple functions with a few hand-picked pa- on which linguistic concepts are system- rameters. This is reasonable, since they were atically challenging for second language largely developed during the 1960s–80s, when learners. Finally, HLR was able to im- people would have had to manage practice sched- prove Duolingo daily student engagement ules without the aid of computers. -
NLP Mᴇᴛʜᴏᴅs ᴛᴏ Aᴜᴛᴏᴍᴀᴛᴇ Lᴀɴɢᴜᴀɢᴇ Lᴇᴀʀɴɪɴɢ ᴛʜʀᴏᴜɢʜ Exᴛᴇɴsɪᴠᴇ Rᴇᴀᴅɪɴɢ Sémagramme Seminar 15 December 2020
Erasmus Mundus Master's Degree in Language and Communication Technologies MSc in Natural Language Processing, University of Lorraine MSc in Language Science and Technology, Saarland University NLP Mᴇᴛʜᴏᴅs ᴛᴏ Aᴜᴛᴏᴍᴀᴛᴇ Lᴀɴɢᴜᴀɢᴇ Lᴇᴀʀɴɪɴɢ ᴛʜʀᴏᴜɢʜ Exᴛᴇɴsɪᴠᴇ Rᴇᴀᴅɪɴɢ Sémagramme Seminar 15 December 2020 Author: Supervisors: Siyana Pavlova Prof. Dr. Günter Neumann Saadullah Amin (unofficial) Prof. Dr. Josef van Genabith Overview ● Goal and Motivation ● Similar Systems ● Building the solution ○ Functional Requirements ○ System Architecture ○ Data ○ Topic classification experiment ● Demo ● System details ● User study ● Future work 2 Goal Develop a language learning application, which uses Extensive Reading to build a vocabulary list and Spaced Repetition to aid users learn vocabulary items. Automation of the content creation and processing should be used wherever possible. 3 Motivation ● Why Extensive Reading (ER) ● Why Spaced Repetition ● Why automation 4 Extensive Reading (ER) ● What is it? ○ The independent reading of large quantity of text for information or pleasure ○ Can be opposed to Intensive Reading ■ Students spending a lot of time reading short, difficult texts under the supervision of a teacher, the emphasis being on the students developing reading and language skills ○ Some argue that learning a foreign language should not be the primary goal [Kaufmann, 2003] ● Why ER? ○ ER beneficial to foreign language acquisition [Mason and Krashen, 1997] ○ More beneficial to reading speed and comprehension than [Image source: https://favpng.com/png_view/book-clip-art-book-ve -
Linguistic Skill Modeling for Second Language Acquisition
Linguistic Skill Modeling for Second Language Acquisition Brian Zylich Andrew Lan [email protected] [email protected] University of Massachusetts Amherst University of Massachusetts Amherst Amherst, Massachusetts Amherst, Massachusetts ABSTRACT 1 INTRODUCTION To adapt materials for an individual learner, intelligent tutoring Intelligent tutors and computer-assisted learning are seeing an systems must estimate their knowledge or abilities. Depending on uptick in usage recently due to advancements in technology and the content taught by the tutor, there have historically been differ- recent shifts to remote learning [39]. These systems have the ability ent approaches to student modeling. Unlike common skill-based to customize their interactions with each individual learner, which models used by math and science tutors, second language acquisi- enables them to provide personalized interventions at a much larger tion (SLA) tutors use memory-based models since there are many scale than one-on-one tutoring. For example, an intelligent math tasks involving memorization and retrieval, such as learning the tutor might identify key skills that a learner has not mastered based meaning of a word in a second language. Based on estimated mem- on items they have attempted and address these skill deficiencies ory strengths provided by these memory-based models, SLA tutors through targeted explanations and additional practice opportunities. are able to identify the optimal timing and content of retrieval prac- Similarly, an intelligent second language acquisition (SLA) tutor tices for each learner to improve retention. In this work, we seek might identify the words (and their contexts) with which a learner to determine whether skill-based models can be combined with tends to struggle and provide more practice opportunities, espe- memory-based models to improve student modeling and especially cially retrieval practice [15]. -
Unbounded Human Learning: Optimal Scheduling for Spaced Repetition
Unbounded Human Learning: Optimal Scheduling for Spaced Repetition Siddharth Reddy Igor Labutov Siddhartha Banerjee Department of Computer Electrical and Computer Operations Research and Science Engineering Information Engineering Cornell University Cornell University Cornell University [email protected] [email protected] [email protected] Thorsten Joachims Department of Computer Science Cornell University [email protected] ABSTRACT 1. INTRODUCTION In the study of human learning, there is broad evidence that The ability to learn and retain a large number of new our ability to retain information improves with repeated ex- pieces of information is an essential component of human posure and decays with delay since last exposure. This plays learning. Scientific theories of human memory, going all the a crucial role in the design of educational software, leading way back to 1885 and the pioneering work of Ebbinghaus [9], to a trade-off between teaching new material and reviewing identify two critical variables that determine the probability what has already been taught. A common way to balance of recalling an item: reinforcement, i.e., repeated exposure this trade-off is spaced repetition, which uses periodic review to the item, and delay, i.e., time since the item was last re- of content to improve long-term retention. Though spaced viewed. Accordingly, scientists have long been proponents repetition is widely used in practice, e.g., in electronic flash- of the spacing effect for learning: the phenomenon in which card software, there is little formal understanding of the periodic, spaced review of content improves long-term reten- design of these systems. Our paper addresses this gap in tion. -
Using Alexa for Flashcard-Based Learning
This is a repository copy of Using Alexa for flashcard-based learning. White Rose Research Online URL for this paper: http://eprints.whiterose.ac.uk/167267/ Version: Published Version Proceedings Paper: Skidmore, L. and Moore, R.K. orcid.org/0000-0003-0065-3311 (2019) Using Alexa for flashcard-based learning. In: Proceedings of Interspeech 2019. Interspeech 2019, 15-19 Sep 2019, Graz, Austria. ISCA , pp. 1846-1850. 10.21437/interspeech.2019-2893 © 2019 ISCA. Reproduced in accordance with the publisher's self-archiving policy. Reuse Items deposited in White Rose Research Online are protected by copyright, with all rights reserved unless indicated otherwise. They may be downloaded and/or printed for private study, or other acts as permitted by national copyright laws. The publisher or other rights holders may allow further reproduction and re-use of the full text version. This is indicated by the licence information on the White Rose Research Online record for the item. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [email protected] including the URL of the record and the reason for the withdrawal request. [email protected] https://eprints.whiterose.ac.uk/ INTERSPEECH 2019 September 15–19, 2019, Graz, Austria Using Alexa for Flashcard-based Learning Lucy Skidmore, Roger K. Moore Speech and Hearing Research Group, Dept. Computer Science, University of Sheffield, UK {lskidmore1,r.k.moore}@sheffield.ac.uk Abstract 3. What additional features could be introduced to Alexa to better facilitate flashcard-based language learning? Despite increasing awareness of Alexa’s potential as an ed- ucational tool, there remains a limited scope for Alexa skills This paper first introduces flashcard-based learning and to accommodate the features required for effective language how it is used in language learning applications.