The role of social networks in students’ learning experiences

The aim of this research is to investigate the role of social networks in computer science education. The Internet shows great potential for enhancing collaboration between people and the role of social software has become increasingly relevant in recent years. This research focuses on analyzing the role that social networks play in students’ learning experiences. The construction of students’ social networks, the evolution of these networks, and their effects on the students’ learning experience in a university environment are examined

To whom to explain and what? : Systematic literature review on empirical studies on Explainable Artificial Intelligence (XAI)

Expectations towards artificial intelligence (AI) have risen continuously because of machine learning models’ evolution. However, the models’ decisions are often not intuitively understandable. For this reason, the field of Explainable AI (XAI) has emerged, which tries to create different techniques to help users understand AI better. As AI’s use spreads more broadly in society, it becomes like a co-worker that people need to understand. For this reason, AI-human interaction in research is of broad and current interest. This thesis outlines the current empirical XAI research literature themes from the human-computer interaction (HCI) perspective. This study's method is an explorative, systematic literature review carried out following the PRISMA (Preferred Research Items for Systematic Reviews) method. In total, 29 articles that concluded an empirical study into XAI from the HCI perspective were included in the review. The material was collected based on database searches and snowball sampling. The articles were analyzed based on their descriptive statistics, stakeholder groups, research questions, and theoretical approaches. This study aims to determine what factors made users consider XAI transparent, explainable, or trustworthy and to whom the XAI research was intended. Based on the analysis, three stakeholder groups to whom the current XAI literature was aimed for emerged: end-users, domain experts, and developers. This study’s findings show that domain experts’ needs towards XAI vary greatly between domains, whereas developers need better tools to create XAI systems. The end-users, on their part, considered case-based explanations unfair and wanted to have explanations that “speak their language”. Also, the results indicate that the effect of current XAI solutions on users’ trust towards AI systems is relatively small or even non-existing. The studies’ direct theoretical contributions and the number of theoretical lenses used were both found out to be relatively low. This thesis’s most immense contribution is to provide a synthesis of the extant empirical XAI literature from the HCI perspective, which previous studies have rarely brought together. Continuing this thesis, researchers can further investigate research avenues such as explanation quality methodologies, algorithm auditing methods, users’ mental models, and prior conceptions about AI.Odotukset tekoälyä kohtaan ovat kohonneet jatkuvasti koneoppimismallien kehittymisen vuoksi. Mallien tekemät päätökset eivät usein ole ihmiskäyttäjälle vaistonvaraisesti ymmärrettävissä. Tätä ongelmaa ratkomaan on syntynyt selittävän tekoälyn tutkimuskenttä, joka luo erilaisia tekniikoita käyttäjien ymmärryksen tueksi. Kun tekoälyn käyttö yhteiskunnassa yleistyy laajemmin, tulee siitä ikään kuin työkaveri, jota ihmisten tulee ymmärtää. Tästä syystä tekoälyn ja ihmisen välisen vuorovaikutuksen tutkiminen on nyt laajan mielenkiinnon kohteena. Tässä pro gradu -tutkielmassa hahmotellaan selittävän tekoälyn tutkimuskentän ajankohtaisia teemoja, ihmisen ja tietokoneen välisen vuorovaikutuksen näkökulmasta. Tutkielman metodi on tutkiva, systemaattinen kirjallisuuskatsaus, ja se suoritettiin seuraten PRISMA-ohjeistusta. Katsaukseen valikoitui yhteensä 29 ihmisen ja tietokoneen vuorovaikutuksen näkökulmasta selittävää tekoälyä empiirisesti tutkinutta artikkelia. Aineisto kerättiin tietokantahakujen ja lumipallo-otannan avulla. Tutkimuksia eriteltiin artikkeleja kuvailevien tietojen, niiden kohdeyleisön, tutkimuskysymysten sekä teoreettisten lähestymistapojen kautta. Tutkielman tarkoituksena on selvittää, millaiset tekijät saivat käyttäjät pitämään tekoälyä läpinäkyvänä, selitettävissä olevana tai luotettavana, sekä kenelle aihepiirin tutkimus oli suunnattu. Analyysin perusteella löytyi kolme ryhmää, joille nykyistä kirjallisuutta on suunnattu: loppukäyttäjät, toimialojen asiantuntijat sekä tekoälyn kehittäjät. Tutkielman tulokset osoittavat, että asiantuntijoiden tarpeet selittävää tekoälyä kohtaan vaihtelevat laajasti toimialojen välillä, kun taas sen kehittäjät kaipaisivat parempia työkaluja tuekseen. Loppukäyttäjien havaittiin pitävän tekoälyn antamia tapauskohtaisia esimerkkejä epäreiluina, ja haluavan juuri heitä puhuttelevia selityksiä. Tulokset ilmaisevat, että nykyisten selittävien tekoälytekniikoiden vaikutukset käyttäjien luottamukseen tekoälyä kohtaan ovat vähäisiä. Tutkimusten tieteellisen panosten ja niiden käyttämien teoreettisten näkökulmien määrän havaittiin olevan suhteellisen pieniä. Tämän tutkielman suurin tieteellinen panos on luoda yhteenveto empiiriseen, selittävän tekoälyn tutkimuskirjallisuuteen, ihmisen ja tietokoneen välisen vuorovaikutuksen näkökulmasta. Tätä näkökulmaa aiempi kirjallisuus on vain harvoin saattanut kokoon. Tutkielma avaa useita näkymiä jatkotutkimukselle, esimerkiksi selitysten laatumetodien, algoritmien auditointimenetelmien, käyttäjien ajatusmallien sekä aiempien käsitysten vaikutusten näkökulmista

Design of an E-learning system using semantic information and cloud computing technologies

Humanity is currently suffering from many difficult problems that threaten the life and survival of the human race. It is very easy for all mankind to be affected, directly or indirectly, by these problems. Education is a key solution for most of them. In our thesis we tried to make use of current technologies to enhance and ease the learning process. We have designed an e-learning system based on semantic information and cloud computing, in addition to many other technologies that contribute to improving the educational process and raising the level of students. The design was built after much research on useful technology, its types, and examples of actual systems that were previously discussed by other researchers. In addition to the proposed design, an algorithm was implemented to identify topics found in large textual educational resources. It was tested and proved to be efficient against other methods. The algorithm has the ability of extracting the main topics from textual learning resources, linking related resources and generating interactive dynamic knowledge graphs. This algorithm accurately and efficiently accomplishes those tasks even for bigger books. We used Wikipedia Miner, TextRank, and Gensim within our algorithm. Our algorithm‘s accuracy was evaluated against Gensim, largely improving its accuracy. Augmenting the system design with the implemented algorithm will produce many useful services for improving the learning process such as: identifying main topics of big textual learning resources automatically and connecting them to other well defined concepts from Wikipedia, enriching current learning resources with semantic information from external sources, providing student with browsable dynamic interactive knowledge graphs, and making use of learning groups to encourage students to share their learning experiences and feedback with other learners.Programa de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Luis Sánchez Fernández.- Secretario: Luis de la Fuente Valentín.- Vocal: Norberto Fernández Garcí

Towards Question-based Recommender Systems

Conversational and question-based recommender systems have gained increasing attention in recent years, with users enabled to converse with the system and better control recommendations. Nevertheless, research in the field is still limited, compared to traditional recommender systems. In this work, we propose a novel Question-based recommendation method, Qrec, to assist users to find items interactively, by answering automatically constructed and algorithmically chosen questions. Previous conversational recommender systems ask users to express their preferences over items or item facets. Our model, instead, asks users to express their preferences over descriptive item features. The model is first trained offline by a novel matrix factorization algorithm, and then iteratively updates the user and item latent factors online by a closed-form solution based on the user answers. Meanwhile, our model infers the underlying user belief and preferences over items to learn an optimal question-asking strategy by using Generalized Binary Search, so as to ask a sequence of questions to the user. Our experimental results demonstrate that our proposed matrix factorization model outperforms the traditional Probabilistic Matrix Factorization model. Further, our proposed Qrec model can greatly improve the performance of state-of-the-art baselines, and it is also effective in the case of cold-start user and item recommendations.Comment: accepted by SIGIR 202

Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey

The increasing number of publications on recommender systems for Technology Enhanced Learning (TEL) evidence a growing interest in their development and deployment. In order to support learning, recommender systems for TEL need to consider specific requirements, which differ from the requirements for recommender systems in other domains like e-commerce. Consequently, these particular requirements motivate the incorporation of specific goals and methods in the evaluation process for TEL recommender systems. In this article, the diverse evaluation methods that have been applied to evaluate TEL recommender systems are investigated. A total of 235 articles are selected from major conferences, workshops, journals, and books where relevant work have been published between 2000 and 2014. These articles are quantitatively analysed and classified according to the following criteria: type of evaluation methodology, subject of evaluation, and effects measured by the evaluation. Results from the survey suggest that there is a growing awareness in the research community of the necessity for more elaborate evaluations. At the same time, there is still substantial potential for further improvements. This survey highlights trends and discusses strengths and shortcomings of the evaluation of TEL recommender systems thus far, thereby aiming to stimulate researchers to contemplate novel evaluation approaches.Laboratorio de Investigación y Formación en Informática Avanzad

Progress in information technology and tourism management: 20 years on and 10 years after the Internet—The state of eTourism research

This paper reviews the published articles on eTourism in the past 20 years. Using a wide variety of sources, mainly in the tourism literature, this paper comprehensively reviews and analyzes prior studies in the context of Internet applications to Tourism. The paper also projects future developments in eTourism and demonstrates critical changes that will influence the tourism industry structure. A major contribution of this paper is its overview of the research and development efforts that have been endeavoured in the field, and the challenges that tourism researchers are, and will be, facing

Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey

The increasing number of publications on recommender systems for Technology Enhanced Learning (TEL) evidence a growing interest in their development and deployment. In order to support learning, recommender systems for TEL need to consider specific requirements, which differ from the requirements for recommender systems in other domains like e-commerce. Consequently, these particular requirements motivate the incorporation of specific goals and methods in the evaluation process for TEL recommender systems. In this article, the diverse evaluation methods that have been applied to evaluate TEL recommender systems are investigated. A total of 235 articles are selected from major conferences, workshops, journals, and books where relevant work have been published between 2000 and 2014. These articles are quantitatively analysed and classified according to the following criteria: type of evaluation methodology, subject of evaluation, and effects measured by the evaluation. Results from the survey suggest that there is a growing awareness in the research community of the necessity for more elaborate evaluations. At the same time, there is still substantial potential for further improvements. This survey highlights trends and discusses strengths and shortcomings of the evaluation of TEL recommender systems thus far, thereby aiming to stimulate researchers to contemplate novel evaluation approaches.Laboratorio de Investigación y Formación en Informática Avanzad

Collaborative filtering recommendation system : a framework in massive open online courses

Massive open online courses (MOOCs) are growing relatively rapidly in the education environment. There is a need for MOOCs to move away from its one-size-fit-all mode. This framework will introduce an algorithm based recommendation system, which will use a collaborative filtering method (CFM). Collaborative filtering method (CFM) is the process of evaluating several items through the rating choices of the participants. Recommendation system is widely becoming popular in online study activities; we want to investigate its support to learning and encouragement to more effective participation. This research will be reviewing existing literature on recommender systems for online learning and its support to learners’ experiences. Our proposed recommendation system will be based on course components rating. The idea was for learners to rate the course and components they have studied in the platform between the scales of 1 – 5. After the rating, we then extract the values into a comma separated values (CSV) file then implement recommendation using Python programming based on learners with similar rating patterns. The aim was to recommend courses to different users in a text editor mode using Python programming. Collaborative filtering will act upon similar rating patterns to recommend courses to different learners, so as to enhance their learning experience and enthusiasm