395 research outputs found

    MyLearningMentor: a mobile App to support learners participating in MOOCs

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    MOOCs have brought a revolution to education. However, their impact is mainly benefiting people with Higher Education degrees. The lack of support and personalized advice in MOOCs is causing that many of the learners that have not developed work habits and self-learning skills give them up at the first obstacle, and do not see MOOCs as an alternative for their education and training. My Learning Mentor (MLM) is a mobile application that addresses the lack of support and personalized advice for learners in MOOCs. This paper presents the architecture of MLM and practical examples of use. The architecture of MLM is designed to provide MOOC participants with a personalized planning that facilitates them following up the MOOCs they enroll. This planning is adapted to learners' profiles, preferences, priorities and previous performance (measured in time devoted to each task). The architecture of MLM is also designed to provide tips and hints aimed at helping learners develop work habits and study skills, and eventually become self-learners.This work has been funded by the Spanish Ministry of Economy and Competitiveness Project TIN2011-28308-C03-01, the Regional Government of Madrid project S2013/ICE-2715, and the postdoctoral fellowship Alliance 4 Universities. The authors would also like to thank Israel Gutiérrez-Rojas for his contributions to the ideas behind MLM and Ricardo García Pericuesta and Carlos de Frutos Plaza for their work implementing different parts of the architecture

    Automated tutoring for a database skills training environment

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    The emergence of educational technology and the growth of the Internet, coupled with the rise in the number of students entering third level education, has led to a surge of online courses offered by universities. These online courses may be part of a traditional classroom based course, or they may act as an entire course by themselves. Student engagement, assessment, feedback and guidance are important parts of any course, but have an added importance for one that is presented online. Together, in the absence of a human tutor, they can greatly aid the student in the learning process. We present an automated skills training system for a database programming environment that will promote procedural knowledge acquisition and skills training. An SQL (Structured Query Language) select statement tutoring tool is an integral part of this. Targeted at students with a prior knowledge of database theory, and as part of a blended learning strategy, the system allows the student to practice SQL querying at his own time and pace. This is achieved by providing pedagogical actions that would be offered by a human tutor. Specifically, we refer to synchronous feedback and guidance based on a personalised assessment. Each of these features is automated and includes a level of personalisation and adaptation. A high-level of interaction and engagement exists between the student and the system. Students assume control of their learning experience

    Pedestrian Pal: A Route Recommendation System for the Android Mobile Phone

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    With mobile phone technology pervading people\u27s everyday lives, using these devices has become prevalent for route recommendations while traveling in unfamiliar locations. Current research works to improve algorithms that calculate efficient and effective routes between two or more points. While expedient travel is necessary in time constraint situations, efficiency is not always required. This paper describes Pedestrian Pal, an application built for the Android mobile phone, which offers route recommendations to users based upon their desired specifications. The recommended routes are not necessarily efficient, but rather are paths based upon collected user ratings, aesthetic interests, and users\u27 inputted parameters. During the development of this project I issued a short survey to 23 individuals to collect data to seed the system and solicit desired functionality for the application. Results of this survey confirmed most pf the design choices and were incorporated into the system. Special design choices were applied during the interface designing phase due to the limited screen real estate of the Android mobile device. Large buttons, simple menus, and familiar layouts do not overwhelm the interface and offer a seamless and intuitive user experience. Upon completion of the application development, ten users participated in testing sessions. These tests required the users to walk through eight separate tasks to examine the system\u27s ease-of-use, fluidity of design, and intuitiveness. A follow-up interview questioned the users about the system\u27s menu navigation, usability, and opportunities for improvement. The user tests provided encouraging feedback that affirmed the design choices as well as discovered faults in the system

    Utilizing implicit feedback data to build a hybrid recommender system

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    Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsIn e-commerce applications, buyers are overwhelmed by the number of products due to the high depth of assortments. They may be interested in receiving recommendations to assist with their purchasing decisions. However, many recommendation engines perform poorly in the absence of community data and contextual data. This thesis examines a hybrid matrix factorisation model, LightFM, representing users and items as linear combinations of their content features’ latent factors. The model embedding item features displays superior user and item cold-start performance. The results demonstrate the importance of selectively embedding contextual data in the presence of cold-start

    User modeling for exploratory search on the Social Web. Exploiting social bookmarking systems for user model extraction, evaluation and integration

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    Exploratory search is an information seeking strategy that extends be- yond the query-and-response paradigm of traditional Information Retrieval models. Users browse through information to discover novel content and to learn more about the newly discovered things. Social bookmarking systems integrate well with exploratory search, because they allow one to search, browse, and filter social bookmarks. Our contribution is an exploratory tag search engine that merges social bookmarking with exploratory search. For this purpose, we have applied collaborative filtering to recommend tags to users. User models are an im- portant prerequisite for recommender systems. We have produced a method to algorithmically extract user models from folksonomies, and an evaluation method to measure the viability of these user models for exploratory search. According to our evaluation web-scale user modeling, which integrates user models from various services across the Social Web, can improve exploratory search. Within this thesis we also provide a method for user model integra- tion. Our exploratory tag search engine implements the findings of our user model extraction, evaluation, and integration methods. It facilitates ex- ploratory search on social bookmarks from Delicious and Connotea and pub- lishes extracted user models as Linked Data
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