Modeling a teacher in a tutorial-like system using Learning Automata

The goal of this paper is to present a novel approach to model the behavior of a Teacher in a Tutorial- like system. In this model, the Teacher is capable of presenting teaching material from a Socratic-type Domain model via multiple-choice questions. Since this knowledge is stored in the Domain model in chapters with different levels of complexity, the Teacher is able to present learning material of varying degrees of difficulty to the Students. In our model, we propose that the Teacher will be able to assist the Students to learn the more difficult material. In order to achieve this, he provides them with hints that are relative to the difficulty of the learning material presented. This enables the Students to cope with the process of handling more complex knowledge, and to be able to learn it appropriately. To our knowledge, the findings of this study are novel to the field of intelligent adaptation using Learning Automata (LA). The novelty lies in the fact that the learning system has a strategy by which it can deal with increasingly more complex/difficult Environments (or domains from which the learning as to be achieved). In our approach, the convergence of the Student models (represented by LA) is driven not only by the response of the Environment (Teacher), but also by the hints that are provided by the latter. Our proposed Teacher model has been tested against different benchmark Environments, and the results of these simulations have demonstrated the salient aspects of our model. The main conclusion is that Normal and Below-Normal learners benefited significantly from the hints provided by the Teacher, while the benefits to (brilliant) Fast learners were marginal. This seems to be in-line with our subjective understanding of the behavior of real-life Students

Modeling a domain in a tutorial-like system using learning automata

The aim of this paper is to present a novel approach to model a knowledge domain for teaching material in a Tutorial-like system. In this approach, the Tutorial-like system is capable of presenting teaching material within a Socratic model of teaching. The corresponding questions are of a multiple choice type, in which the complexity of the material increases in difficulty. This enables the Tutorial-like system to present the teaching material in different chapters, where each chapter represents a level of difficulty that is harder than the previous one. We attempt to achieve the entire learning process using the Learning Automata (LA) paradigm. In order for the Domain model to possess an increased difficulty for the teaching Environment, we propose to correspondingly reduce the range of the penalty probabilities of all actions by incorporating a scaling factor μ. We show that such a scaling renders it more difficult for the Student to infer the correct action within the LA paradigm. To the best of our knowledge, the concept of modeling teaching material with increasing difficulty using a LA paradigm is unique. The main results we have obtained are that increasing the difficulty of the teaching material can affect the learning of Normal and Below-Normal Students by resulting in an increased learning time, but it seems to have no effect on the learning behavior of Fast Students. The proposed representation has been tested for different benchmark Environments, and the results show that the difficulty of the Environments can be increased by decreasing the range of the penalty probabilities. For example, for some Environments, decreasing the range of the penalty probabilities by 50% results in increasing the difficulty of learning for Normal Students by more than 60%

Balanced difficulty task finder: an adaptive recommendation method for learning tasks based on the concept of state of flow

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An Artificial Neural Network technique for on-line hotel booking

In this paper the use of Artificial Neural Networks (ANNs) in on-line booking for hotel industry is investigated. The paper details the description, the modeling and the resolution technique of on-line booking. The latter problem is modeled using the paradigms of machine learning, in place of standard `If-Then-Else' chains of conditional rules. In particular, a supervised three layers MLP neural network is adopted, which is trained using information from previous customers' reservations. Performance of our ANN is analyzed: it behaves in a quite satisfactory way in managing the (simulated) booking service in a hotel. The customer requires single or double rooms, while the system gives as a reply the confirmation of the required services, if available. Moreover, we highlight that using our approach the system proposes alternative accommodations (from two days in advance to two days later with respect to the requested day), in case rooms or services are not available. Numerical results are given, where the effectiveness of the proposed approach is critically analyzed. Finally, we outline guidelines for future research.On-line booking; hotel reservation; machine learning; supervised multilayer perceptron networks

Effects of regular use of scalable, technology enhanced solution for primary mathematics education

Mathematics is one of the key subjects in any school curriculum and most teachers agree that mathematical skills are important for students to master. There is an abundance of research in learning mathematics and a consensus exists among researchers that technology can enhance the learning process. However, many factors need to be taken into consideration when introducing technology into teaching mathematics. Developing a more natural collaboration between learning technology experts, teachers, and students ensures all stakeholders are considered. Involving teachers early on helps develop enduring commitment to innovations and practical solutions. Moreover, creating a culture of collaboration between experts in the field and teachers brings to bear the best of what both worlds have to offer. This thesis synthesizes six papers and offers additional findings that focus on how technology experts can collaborate with elementary teachers to improve student learning outcomes. We focus on managing educational change in ways that improve the sustainability of innovations. We also explore how technical and teaching experts co-create effective lesson plans. In one of the six papers we collected and reported teachers’ responses to survey questions covering typical usage patterns on a platform. Teachers’ direct feedback was collected and incorporated to improve technical solutions. Moreover, one study was conducted abroad to measure the effect of culture on the teaching and learning process. Evidence of effectiveness of technologically enhanced lessons and corresponding homework was based on multiple studies in grades 1 - 3, covering 379 students. The effectiveness of educational technology was measured based on two variables: student performance in mathematics, based on the learning objectives specified in the curriculum, and arithmetic fluency measured by how rapidly and accurately students solved basic arithmetic operations. Statistically significant findings show that educational technology can improve two target variables when comparing students who did not use educational technology to students who did. An additional effect size analysis was conducted to verify and compare results with previous research. Based on these results, platform use produced the same or better effect than previous studies. Based on teacher feedback and user growth on the platform, we managed to integrate technology into the regular school classroom in meaningful and sustainable ways. We were clearly able to support teachers in their practice in a manner that resulted in noticeable student achievement gains. A survey revealed a need to emphasize new features that were introduced to the platform in teacher training programs. Teachers also reported having a positive attitude towards the platform and the initiative gained wide acceptance among their peers.Matematiikka on yksi tärkeimmistä kouluaineista pelkästään tuntimääräisesti mitattunakin. Matematiikan osaamista ja oppimista pidetään yleisesti tärkeänä ja arvostettuna taitona. Matematiikan oppimisesta on valtavasti tutkimusta ja tutkijoiden keskuudessa vallitsee yhteisymmärrys tietotekniikan positiivisista mahdollisuuksista edistää matematiikan oppimista. Tietotekniikan ja oppimisen vuorovaikutus on kuitenkin monisyinen vyyhti ja sen onnistunut hyödyntäminen vaatii tutkijoiden, opettajien ja oppilaiden välistä tiivistä ja vuorovaikutteista yhteistyötä. Uusien innovaatioiden ja kokeilujen onnistumiselle ja niihin sitoutumiselle luodaan vahva pohja, kun opettajat otetaan mukaan kehitystyöhön ensimetreiltä lähtien. Tällaisen tiiviin yhteistyökulttuurin vaaliminen mahdollistaa käytännön työn ja teorian vahvuuksien hyödyntämisen. Tämä väitöstyö koostuu kuudesta artikkelista. Artikkelit kuvaavat, kuinka tutkijat ja opettajat työskentelivät yhdessä parantaakseen oppilaiden matematiikan oppimista. Tavoitteenamme oli muuttaa koulun käytänteitä pitkäjänteisesti ja kestävällä tavalla. Tutkimme kuinka tutkijat ja opettajat pystyivät yhdessä luomaan onnistuneita ja tehokkaita oppimiskokonaisuuksia. Opettajat olivat koko ajan kehitystyön keskiössä. Yhdessä kuudesta artikkelista tutkittiin kyselytutkimuksen avulla opettajien kokemuksia ja käyttötottumuksia. Näitä vastauksia hyödynnettiin teknisessä kehitystyössä ja hyvien käytänteiden hiomisessa. Yksi väitöskirjan tutkimuksista tehtiin ulkomailla opetus- ja oppimiskulttuureista vaikutusten huomioimiseksi. Sähköisten oppituntien ja kotitehtävien vaikuttavuuden arviointi perustuu useisiin 1.-3. luokilla tehtyihin tutkimuksiin ja kaikkiaan 379 oppilaan vastauksiin. Sähköisten oppituntien vaikuttavuutta arvioitiin kahden eri mittarin perusteella. Ensin matematiikan taitojen perusteella, eli kuinka hyvin kunkin luokka-asteen oppimistavoitteet olivat täyttyneet ja myöhemmin myös laskusujuvuuden perusteella, eli kuinka nopeasti ja tarkasti oppilaat pystyivät laskemaan peruslaskutoimituksia. Tulokset osoittavat, että opetusteknologian avulla pystytään parantamaan oppilaiden suoriutumista edellä mainittujen osa-alueiden osalta verrattuna oppilaisiin, jotka eivät käyttäneet opetusteknologiaa. Tulokset olivat tilastollisesti merkitseviä. Näiden tulosten varmistamiseksi laskettiin vaikuttavuuden suuruus ja sitä verrattiin aiempiin alan tutkimuksiin. Tulosten perusteella sähköisillä oppitunneilla oli sama tai parempi vaikuttavuus kuin aiemmissa tutkimuksissa. Opettajien palautteiden ja kasvavan käyttäjämäärän perusteella voidaan sanoa, että onnistuimme tavoitteessamme integroida opetusteknologiaa mielekkäällä tavalla osaksi koulutyötä. Onnistuimme myös tukemaan ja auttamaan opettajia opetustyössään ja samalla merkittävästi parantamaan oppilaiden suoriutumista. Kyselytutkimuksen perusteella huomasimme, että uusien ominaisuuksien kouluttamiseen tulee kiinnittää enemmän huomiota. Samassa tutkimuksessa opettajat raportoivat olevansa tyytyväisiä alustaan ja sähköiset oppitunnit näyttävät saaneen vankan jalansijan suomalaisessa opettajakunnassa

On Supervisor Synthesis via Active Automata Learning

Our society\u27s reliance on computer-controlled systems is rapidly growing. Such systems are found in various devices, ranging from simple light switches to safety-critical systems like autonomous vehicles. In the context of safety-critical systems, safety and correctness are of utmost importance. Faults and errors could have catastrophic consequences. Thus, there is a need for rigorous methodologies that help provide guarantees of safety and correctness. Supervisor synthesis, the concept of being able to mathematically synthesize a supervisor that ensures that the closed-loop system behaves in accordance with known requirements, can indeed help.This thesis introduces supervisor learning, an approach to help automate the learning of supervisors in the absence of plant models. Traditionally, supervisor synthesis makes use of plant models and specification models to obtain a supervisor. Industrial adoption of this method is limited due to, among other things, the difficulty in obtaining usable plant models. Manually creating these plant models is an error-prone and time-consuming process. Thus, supervisor learning intends to improve the industrial adoption of supervisory control by automating the process of generating supervisors in the absence of plant models.The idea here is to learn a supervisor for the system under learning (SUL) by active interaction and experimentation. To this end, we present two algorithms, SupL*, and MSL, that directly learn supervisors when provided with a simulator of the SUL and its corresponding specifications. SupL* is a language-based learner that learns one supervisor for the entire system. MSL, on the other hand, learns a modular supervisor, that is, several smaller supervisors, one for each specification. Additionally, a third algorithm, MPL, is introduced for learning a modular plant model.The approach is realized in the tool MIDES and has been used to learn supervisors in a virtual manufacturing setting for the Machine Buffer Machine example, as well as learning a model of the Lateral State Manager, a sub-component of a self-driving car. These case studies show the feasibility and applicability of the proposed approach, in addition to helping identify future directions for research