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Name URL Cost Address Year Created Type Audiences Technologies User count Creators Description Evolution Tech Description Uses Reasons Managers Strategies Pros Limitations Difficulties Publications
Khan Academy analytics https://www.khanacademy.org/ Free USA 2011 Academic - School Preschool (0-5 years), Primary (6-11 years old) and Secondary (12-17 years old) Analytics 0 David Hu Khan Academy offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. With Khan Academy, teachers can identify gaps in their students' understanding, tailor instruction, and meet individual student needs. S.O. Adaptive learning S.O. http://david-hu.com/2011/11/02/how-khan-academy-is-using-machine-learning-to-assess-student-mastery.html
UTIFEN https://www.utifen.org Free Niamey (Niger) 2016 Academic - School Teachers Analytics 24,000 Chaire de recherche du Canada sur les technologies de l'information et de la communication (TIC) UTIFEN is a smart, adaptive online training platform. It adapts learning materials and sends personalized text messages based on each participant’s progress. Personalized teacher training (using computer, tablet or phone), collaboration between participants. N/A Adaptive learning. Adapts learning materials. Personalized text messages. N/A Karsenti, T. et al. Le projet UTIFEN, pour la formation des enseignants du Niger. Bulletin de liaison RIFEFF (2018)
First Class https://beyond.psu.edu/firstclass/ Free 801, États-Unis Academic - School Teachers Natural language processing, Visual recognition and Analytics 0 Pennsylvania State University First Class is an immersive experience platform that brings the power of augmented reality to teacher education. First Class features a virtual K–12 class with six AI students, allowing aspiring teachers to use movement, voice, and gestures to interact with and react to the “virtual students” as the latter engage in a variety of behaviors. Immersive experience platform for aspiring teachers s.o. Practice authentically, Virtual students, Valuable feedback to users during and after their experience N/A
AutoTutor https://www.memphis.edu/iis/projects/autotutor.php Free Institute of Intelligent Systems, University of Memphis, 365 Innovation Dr, Memphis, TN 38152, USA Academic - School Post-secondary (18 years and over) Natural language processing, Visual recognition, Speech Recognition and Analytics 0 The University of Memphis AutoTutor is a computer tutor that helps students learn by holding a conversation in natural language. It simulates a tutorial dialogue between human tutors and students as they work step-by-step through online tasks. AutoTutor tracks the cognition and emotions of the student and responds in a manner that adapts to the student. Emotions are recognized by the dialogue patterns, facial expressions, and body posture of the student. Learning physics and computer skills N/A Conversations in natural language (written or spoken). Conversational Agent adapts to the student, encouraging students to develop detailed responses and deep understanding N/A Graesser, A. C., Li, H., & Forsyth, C. (2014). Learning by communicating in natural language with conversational agents. Current Directions in Psychological Science, 23(5), 374-380.
Smart enseigno https://www.smartenseigno.fr/ Free Educlever PARIS, 59 rue Benoit Malon, 94250 Gentilly, France 2019 Academic - School Primary (6-11 years old) Analytics 0 EDUCLEVER, CABRILOG, INRIA WIMMICS, LUDOTIC A rich, evolving body of mathematical resources for CP, CE1 and CE2 classes [in the French school system], SmartEnseigno offers students a range of learning activities reflective of their needs (adaptive learning). The teacher has a dedicated digital assistant (smart dashboard). Adaptive learning, smart dashboard, expert system, reasoner, machine learning Learning mathematics N/A Personalized learning, student monitoring N/A
The MIT Education Arcade https://education.mit.edu/project-type/games/ Free 700 Technology Square, Suite 328, Cambridge, MA 02139, USA 2018 Academic - School General public Analytics 0 The MIT Scheller Teacher Education Program We work with educators to make sure our games tie into their math and science curricula. The games we develop can be played on computers or mobile devices and are used both inside and outside the classroom. Serious game Mathematics ans sciences N/A Engage learners to interact actively for longer periods of time, Maximize motivation N/A Groff, J. S. (2018). The potentials of game‐based environments for integrated, immersive learning data. European Journal of Education, 53(2), 188-201.
Alloprof https://www.alloprof.qc.ca Free Canada 1996 Academic - School Primary (6-11 years old), Secondary (12-17 years old), Teachers and Other education actors Analytics 0 Alloprof Alloprof engages Quebec students and their parents in educational success by offering them free professional and stimulating academic support. Tools that save students time and help them succeed with homework and lessons include videos, pedagogic sheets, texting, exam preparation tips and advice, and interactive exercises. Through the integration of an artificial intelligence of the type chatbot, the immersive game 'Spellers' allows players to improve their writing skills in English. AI to maintain a personalized relationship with learners, Immersive game to help students learn English as a second language. Helping students with homework and fostering learning N/A Helping students with homework , Fostering and gamifying learning, motivating students, Equipping parents. N/A Karsenti, T. (2015). Quel est le rôle d’Allô prof dans la persévérance et la réussite scolaires des élèves? Étude auprès de 6659 acteurs scolaires (élèves, enseignants, directions et parents). Rapport synthèse de recherche. Montréal, QC : CRIFPE. Tanguay, M. A. (2017). Stratégie numérique : Pour mieux engager l’élève et les acteurs de l’éducation. Alloprof. URL: blogue.alloprof.qc.ca/wp-content/uploads/2017/02/AP_memoire_Strategie_numerique_vf-4.pdf
Apereo Open Learning Analytics Platform https://www.apereo.org/projects/learning-analytics-processor Free USA 2015 Academic - School Post-secondary (18 years and over), Teachers and Researchers Analytics 0 Apereo Foundation and Society for Learning Analytics Research (SoLAR) Learning Analytics Processor is a project that aims to accelerate the future of predictive learning analytics by developing a flexible and highly scalable tool. A powerful big data tool, a library of open source predictive models can be shared in higher education at no cost so institutions can work together to improve these models over time. Analyze student learning. N/A Better monitoring of students. Personalized learning. N/A Flanagan, B., & Ogata, H. (2017, November). Integration of learning analytics research and production systems while protecting privacy. In The 25th International Conference on Computers in Education, Christchurch, New Zealand (pp. 333-338).
Kaspar http://www.herts.ac.uk/kaspar Free University of Hertfordshire, Hatfield, Hertfordshire, AL10 9AB, UK 2005 Academic - School Preschool (0-5 years), Primary (6-11 years old) and Learners with special needs Robotics 0 University of Hertfordshire Kaspar is a humanoid robot that acts as a social companion to improve the lives of children with autism and other communication difficulties. By interacting and behaving in a child-like way, Kaspar helps teachers and parents support children with autism to overcome the challenges they face in socialising and communicating with others. Helping autistic children N/A Improve communication, interpersonal skills and emotional well-being. Teach children with autism about physical interactions. Facilitate collaborative play between autistic children. Help teachers and parents support children with autism. N/A Huijnen, C. A., Lexis, M. A., & de Witte, L. P. (2016). Matching robot KASPAR to autism spectrum disorder (ASD) therapy and educational goals. International Journal of Social Robotics, 8(4), 445-455.
Navigator https://gooru.org/about Free 350 Twin Dolphin Dr, Suite 115, Redwood City, CA 94065, USA Academic - School Post-secondary (18 years and over) and Teachers Analytics 0 Gooru Learning With Navigator, learners experience a GPS-like journey for learning, instructors monitor their learners and engage them with classroom practices, administrators track performance and scale success, and content providers develop a greater audience. Navigator uses AI techniques to individualize the learning experience and make individual learning at scale possible by understanding each learner through computing their preferences, context, and performance. Adaptive learning, Real-time Feedback N/A Adaptive learning, Real-time Feedback N/A Songer, N. B., Newstadt, M. R., Lucchesi, K., & Ram, P. (2020). Navigated learning: An approach for differentiated classroom instruction built on learning science and data science foundations. Human Behavior and Emerging Technologies, 2(1), 93-105.
iTalk2Learn https://www.italk2learn.com/ Free University College London, Gower Street Wc1e 6bt London, UK 2012 Academic - School Primary (6-11 years old) Natural language processing, Speech Recognition and Analytics 0 Projet de recherche, Franctions Lab , financé par l'Union Européenne iTalk2Learn is a learning platform that supports maths learning for students aged 5 to 11. It facilitates interaction in different modalities including speech, as well as multiple representations which can be manipulated and reasoned with when learning fractions. The system takes into account all historical performance across an entire student base as well as the behavioural patterns of each individual student in order to adapt more intelligently to their needs. It is able to interact with and respond to a student’s speech throughout a tutoring session to detect patterns of behaviour, attitude to the learning situation and affective states. Machine learning, Natural language processing, Bayesian networks (an AI technique) to detect and improve the emotional states of the student, Speech recognition to interact with and respond to a student's speech. Mathematics learning N/A Learn in a more natural way, Individualized learning, Detect and improve the emotional states of the student, Provide context specific feedback N/A https://www.italk2learn.com/deliverables-and-publications/publications/
LightSide http://ankara.lti.cs.cmu.edu/side/ Free 5000 Forbes Avenue, Pittsburgh, PA 15213 Academic - School Researchers Analytics 0 Carnegie Mellon University LightSIDE is a free and open text mining toolbench, which is used both for teaching and for research. It provides a convenient GUI environment for novice users of text classification technology easily run text extraction and classification experiments. Machine learning algorightms that can learn to extract features based on training examples entered by humans Text mining toolbench used for teaching and for research N/A Text mining toolbench, freely available for research and education N/A Mayfield, E. et Rosé, C. P. (2013). LightSIDE: Open Source Machine Learning for Text Accessible to Non-Experts, Invited chapter in the Handbook of Automated Essay Grading, Routledge Academic Press.
Assistments https://new.assistments.org/ Free Worcester, Massachusetts (USA) 2003 Academic - School Secondary (12-17 years old), Teachers and Learners with special needs Expert systems and Analytics 0 ASSISTments Assistments is a platform that helps teachers create homework, supports students with immediate feedback and advice, and provides teachers with student performance analyses, suggesting where to focus their teaching time. AI to identify concepts that should be gone over again by the teacher. Learning mathematics N/A Immediate feedback on homework, analysis of student performance. Free. Appropriation of the tool. Connection with Google Classroom or Canvas. Roschelle, J., Feng, M., Murphy, R. F., et Mason, C. A. (2016). Online mathematics homework increases student achievement. AERA Open, 2(4), 2332858416673968.
Betty’s Brain https://wp0.vanderbilt.edu/oele/bettys-brain/ Free Vanderbilt University, Nashville, Tennessee 37240, USA Academic - School Secondary (12-17 years old) Natural language processing and Analytics 0 The Teachable Agents Group (à Vanderbilt University) Betty’s Brain is a computer-based learning environment that utilizes the learning-by-teaching paradigm to engage students in learning about science topics. Students are encouraged to teach a fellow student, in fact a virtual agent, called Betty. students are supported to teach Betty, then to query Betty to see how much she has understood, and finally to quiz Betty to see how well she does on questions generated automatically by the system, many of which the student may not have considered. Additionally, the system monitors the students’ actions, guides and mentors them as they go about their learning and teaching tasks. Science learning N/A Development of metacognitive skills, strengthen knowledge of river ecosystems N/A
TeachLivE http://teachlive.org/about/about-teachlive/ Free 12494 University Blvd., Orlando, FL 32816, USA 2013 Academic - School Post-secondary (18 years and over) and Teachers Natural language processing, Speech Recognition and Analytics 0 UCF Center for Research in Education Simulation Technology TLE TeachLivE™ is a mixed-reality classroom with simulated students that provides teachers the opportunity to develop their pedagogical practice in a safe environment that doesn’t place real students at risk. Experiential learning, avatar-based characters, mimic human nuances, voice, vocabulary, gestures Teacher training N/A Teachers can learn the instruction and management skills, Practicing teachers can hone and refine their skills, Examining how participants’ respond to a changing classroom environment, Experimenting with a new teaching idea poses no danger to the learning of any real student N/A Dieker, L. A., Rodriguez, J. A., Lignugaris/Kraft, B., Hynes, M. C., et Hughes, C. E. (2014). The potential of simulated environments in teacher education: Current and future possibilities. Teacher Education and Special Education, 37(1), 21-33.
Completing the loop https://le.unimelb.edu.au/news/articles/learning-analytics-with-the-loop-tool Free Australie 2016 Academic - School Post-secondary (18 years and over) Analytics 0 Université de Melbourne Platform that takes usage data from an online platform and displays it in an easy-to-understand way to give a better of idea of ​​student success and engagement. Also provides tools to help users measure the impact of events over the course of the term. For example, did students access the material before or after the lecture? How was the homework submitted? Data analysis N/A Makes it easy to monitor students and personalize learning N/A Bakharia, A. et al. 2016, April. A conceptual framework linking learning design with learning analytics. Dans Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (pp. 329-338).
Writing Pal http://www.adaptiveliteracy.com/writing-pal Free Payne Hall 108, 1000 S. Forest Mall, Tempe, AZ 85287, US Academic - School Teachers Visual recognition and Analytics 0 SoLET Lab à Arizona State University Writing Pal is a web-based software tool developed to provide a means of automatically scoring essays in the same way as a teacher might while also providing writing strategy instruction, game-based practice and individualized formative feedback to help students improve their writing proficiency. Learning English (Writing) N/A Receive a score and formative feedback, assistant N/A Roscoe, R. D., Allen, L. K., Weston, J. L., Crossley, S. A., & McNamara, D. S. (2014). The Writing Pal intelligent tutoring system: Usability testing and development. Computers and Composition, 34, 39-59.
RoboTutor https://www.cmu.edu/scs/robotutor/what-is-robotutor/index.html Free CMU-RI-NSH 3113, 5000 Forbes Avenue Pittsburgh, PA 15213, USA 2015 Academic - School Primary (6-11 years old) and Learners with special needs Natural language processing, Visual recognition and Speech Recognition 0 Carnegie Mellon University RoboTutor is an open-source Android tablet app that enables children ages 7-10 with little or no access to schools to learn basic reading, writing, and arithmetic without adult assistance. Each RoboTutor session consists of a series of activities selected by the child from a few reading, comprehension, or numeracy activities at his or her current level, with a sequence of items for the child to perform. RoboTutor adapts to each child by assessing performance automatically, providing individualized help and feedback, adjusting its estimate of his or her level, and proceeding accordingly. It was one of five $1M Finalists in the $15M Global Learning XPRIZE competition. Speech and handwriting recognition, Automated question generation, Educational data mining. learning reading, writing and basic arithmetic without the help of an adult. N/A Practicing with scaffolding and feedback, Eliciting active processing, Adapting to individual student, Open source. N/A Saxena, M., Pillai, R. K., & Mostow, J. (2018, April). Relating Children’s Automatically Detected Facial Expressions to Their Behavior in RoboTutor. In Thirty-Second AAAI Conference on Artificial Intelligence.
Crystal Island http://projects.intellimedia.ncsu.edu/crystalisland/ Free North carolina state university, Raleigh, NC 27695, USA Academic - School Post-secondary (18 years and over) and Teachers Analytics 4,000 North carolina state university Crystal Island is a game-based learning environment for middle grade science and literacy. In the game, students play the role of a medical field detective investigating a mysterious infectious disease outbreak affecting a team of scientists on a remote island. While exploring the island research camp from an immersive first-person viewpoint, students utilize critical thinking skills to solve problems about the source and impact of the outbreak, test hypotheses using virtual laboratory equipment, and complete a diagnosis worksheet to record their findings. AI techniques to create an intelligent game-based learning environment, to provide automated supportive feedback, to build AI-driven autonomous non-player characters (companion agents) Learning science and language arts N/A Motivating and engaging context for students to solve a scientific problem, Develop the ability to interpret complex information texts. complete classroom instruction, test bench for researching intelligent tutoring systems, Free. N/A http://projects.intellimedia.ncsu.edu/crystalisland/publications
Dytective https://www.changedyslexia.org/en Free Carrer del Dr. Trueta, 183, 08005 Barcelona, Espagne Academic - School Preschool (0-5 years), Primary (6-11 years old) and Learners with special needs Analytics 270,000 Change Dyslexia Dytective is a tool that can detect dyslexia in about 15 minutes. It also provides exercises in the form of games for dyslexic learners to help them overcome their writing and reading challenges. The app includes a mix of online testing and applications for associated predictive machine learning. Machine learning models to predict reading and writing difficulties by watching how people interact with a linguistic web-based game. Detecting dyslexia and helping dyslexic learners overcome their challenges N/A Detects dyslexia, Helps learners overcome their writing and reading challenges. N/A Rello, L., Ballesteros, M., Ali, A., Serra, M., Alarcón, D., & Bigham, J. P. (2016). Dytective: Diagnosing risk of dyslexia with a game. Proc. Pervasive Health, 16.
OU Analyse https://analyse.kmi.open.ac.uk/ Free Royaume-Uni 2017 Academic - School Post-secondary (18 years and over) and Learners with special needs Analytics 0 Knowledge Media Institute Pilot project based on machine learning for early identification of students at risk of failing. All students at risk of failing have course tutors and student support teams available to them every week for appropriate support. Data analysis Identify students at risk of failure. N/A Detects students at risk of failure. N/A Rienties, B., Cross, S. et Zdrahal, Z., 2017. Implementing a learning analytics intervention and evaluation framework: What works?. In Big data and learning analytics in higher education (pp. 147-166). Springer, Cham.