Recommendation System for News Reader

Recommendation Systems help users to find information and make decisions where they lack the required knowledge to judge a particular product. Also, the information dataset available can be huge and recommendation systems help in filtering this data according to users‟ needs. Recommendation systems can be used in various different ways to facilitate its users with effective information sorting. For a person who loves reading, this paper presents the research and implementation of a Recommendation System for a NewsReader Application using Android Platform. The NewsReader Application proactively recommends news articles as per the reading habits of the user, recorded over a period of time and also recommends the currently trending articles. Recommendation systems and their implementations using various algorithms is the primary area of study for this project. This research paper compares and details popular recommendation algorithms viz. Content based recommendation systems, Collaborative recommendation systems etc. Moreover, it also presents a more efficient Hybrid approach that absorbs the best aspects from both the algorithms mentioned above, while trying to eliminate all the potential drawbacks observed

Integrating Technology With Student-Centered Learning

Reviews research on technology's role in personalizing learning, its integration into curriculum-based and school- or district-wide initiatives, and the potential of emerging digital technologies to expand student-centered learning. Outlines implications

weSPOT: A personal and social approach to inquiry-based learning

weSPOT is a new European initiative proposing a novel approach for personal and social inquiry-based learning in secondary and higher education. weSPOT aims at enabling students to create their mash-ups out of cloud based tools and services in order to perform scientific investigations. Students will also be able to share their inquiry accomplishments in social networks and receive feedback from the learning environment and their peers. This paper presents the research framework of the weSPOT project, as well as the initial inquiry-based learning scenarios that will be piloted by the project in real-life educational settings

Learner- vs. expert-constructed outlines: Testing the associations with L2 text comprehension and multiple intelligences

Cognitive organizers (COs) are text aids which represent objects, concepts, and their relations by the use of symbols and spatial arrangements without adding to semantic content. The present study examines language learners’ text comprehension through outlines, a popular CO, compared with text-only condition, and further investigates the effect of learner-constructed outlines (i.e., systematic note-taking) and expert-constructed outlines (i.e., readymade displays) on comprehension. Finally, the predictive power of multiple intelligences (MI) across different input modalities is scrutinized. Following stratified random sampling, a total of 111 EFL undergraduates were divided into text-only (receiving a text twice), expert-constructed (the text followed by an outline), and learner-constructed (the text followed by an outline to be drawn up by the learner) groups. A TOEFL examination, a 1218-word expository text on systematic sleep disorder, a follow-up reading comprehension test, and a multiple intelligences inventory constituted the data collection measures. The results of multiple regression and ANOVA were as follows: (a) COs lead to more content recall than text displays; (b) expert-constructed and learner-constructed outlines are equally effective; (c) MI significantly predicts the groups’ reading comprehension; (d) interpersonal and intrapersonal intelligences are significant correlates of text-only groups’ performance; and (e) visual, verbal, and intrapersonal intelligences are significantly associated with learner-constructed groups’ reading scores. The study offers several implications for theory and practice

Improving Second Language Lexical Acquisition Through Personalization and Contextualization: A Look at Intrinsic Cognitive Load Reduction Strategies

Cognitive load reduction strategies traditionally seek to reduce the amount of extraneous mental effort required of the learner. Researchers, through effective instructional design, seek to eliminate load-causing agents that are extraneous to the learning topic at hand. However, cognitive load theory research has now shifted to also include the exploration of strategies that seek to reduce the inherent complexities of the target topic itself. The current study seeks to apply two such intrinsic cognitive load reduction strategies—personalization and contextualization. Previous research suggests that cognitive load can be reduced by personalizing the learning environment, which serves to meet the interests of each learner as well as to provide a familiar environment, or prior knowledge script, for the learner. By utilizing instructional materials for which learners already have an established script, personalized materials are able to reduce the number of novel elements that must be individually processed by the learner, and by so doing, effectively reduce cognitive load. Research also suggests that personalized learning environments can also be more intrinsically motivating for learners, a tenant that is again assessed in the current study. Intrinsic cognitive load reduction research likewise suggests that new topics be presented serially, and in isolation from confounding authentic contexts when possible, in order to reduce the number of elements that must be simultaneously processed that might otherwise outstrip learners’ available cognitive resources. Contrarily, second language acquisition research suggests that new target lexical items are best learned through inferring a new term’s meaning through a rich authentic context. Studies contend that learners are able to map a lexicon’s form to its meaning most effectively when new terms are interpreted through highly contextualized imbedded learning environments. The current study sought to determine how a multimedia tutorial’s level of personalization and contextualization could be manipulated to improve foreign language lexical learning, reduce cognitive load, and improve motivation for learning. A sample population of beginning college Spanish language learners (n = 128) was subjected to four different versions of a multimedia tutorial (i.e., personalized-contextualized, personalized-decontextualized, generic-contextualized, and generic-decontextualized). Following the tutorial, learners were tested for their ability to retain the novel content and transfer this content to new environments. Additionally, learners were asked to rank their motivation for learning the new topic, and the cognitive load endured during the learning and testing processes. Achievement results showed a significant interaction effect for personalization and contextualization. When learners were asked to solve a complex problem utilizing the new target lexical terms, personalized-contextualized learners and generic-decontextualized learners were more effective than their contemporaries. A significant interaction effect was also demonstrated for cognitive load, which suggested that personalized-contextualized and generic-decontextualized learners suffered less cognitive load when completing a complex task than other learners. Finally, results showed a positive effect for motivation demonstrated by learners who were exposed to a personalized learning environment as opposed to a generic learning environment

A framework for design engineering education in a global context

This paper presents a framework for teaching design engineering in a global context using innovative technologies to enable distributed teams to work together effectively across international and cultural boundaries. The DIDET Framework represents the findings of a 5-year project conducted by the University of Strathclyde, Stanford University and Olin College which enhanced student learning opportunities by enabling them to partake in global, team based design engineering projects, directly experiencing different cultural contexts and accessing a variety of digital information sources via a range of innovative technology. The use of innovative technology enabled the formalization of design knowledge within international student teams as did the methods that were developed for students to store, share and reuse information. Coaching methods were used by teaching staff to support distributed teams and evaluation work on relevant classes was carried out regularly to allow ongoing improvement of learning and teaching and show improvements in student learning. Major findings of the 5 year project include the requirement to overcome technological, pedagogical and cultural issues for successful eLearning implementations. The DIDET Framework encapsulates all the conclusions relating to design engineering in a global context. Each of the principles for effective distributed design learning is shown along with relevant findings and suggested metrics. The findings detailed in the paper were reached through a series of interventions in design engineering education at the collaborating institutions. Evaluation was carried out on an ongoing basis and fed back into project development, both on the pedagogical and the technological approaches

Learning on demand: dynamic creation of customized, coherent eLearning experiences

This paper presents a conceptual model concerning automatic creation of coherent eLearning experiences through dynamic aggregation of heterogeneous learning objects. The model uses detailed user profiles as the main key to create customized learning content, tailored to each user’s needs, preferences and skills. Aggregation of heterogeneous learning objects may result in a very incoherent learning sequence. Therefore, the model incorporates a method to improve the learning coherence of the generated course. The compromise between customization and coherence is fully adjustable from maximum coherence to maximum customization