Meeting of the Minds: Online Matching Program for Teaching and Learning Styles?

Although there are many disagreements on the major causes of problems in the educational system in the United States, few would disagree that the importance of ensuring the success of our students is not only a moral obligation to our youth, but imperative to our national resilience. And, as a nation, we are responding to this urgent need. The College Completion Agenda mandates that 55% of the nation’s 25-to-35-year olds will be degree holders by the year 2025 (Lee & Rawls, 2010). But how do we accomplish the ambitious challenge and a necessary aspiration to elevate America’s status as a world leader in educational attainment among its citizenry? Innovative ideas and technologies that can counterbalance the barriers that threaten student success must be identified and advanced. The purpose of this article is to discuss the opportunity to leverage the marvel of online phenomena and technology in innovative ways to help students reach their full potential. Specifically, we introduce a new conceptual tool that replicates the power of social networking online matching services to link student learning preference with faculty instructional style.     

Statistical biases in Information Retrieval metrics for recommender systems

There is an increasing consensus in the Recommender Systems community that the dominant error-based evaluation metrics are insufficient, and mostly inadequate, to properly assess the practical effectiveness of recommendations. Seeking to evaluate recommendation rankings—which largely determine the effective accuracy in matching user needs—rather than predicted rating values, Information Retrieval metrics have started to be applied for the evaluation of recommender systems. In this paper we analyse the main issues and potential divergences in the application of Information Retrieval methodologies to recommender system evaluation, and provide a systematic characterisation of experimental design alternatives for this adaptation. We lay out an experimental configuration framework upon which we identify and analyse specific statistical biases arising in the adaptation of Information Retrieval metrics to recommendation tasks, namely sparsity and popularity biases. These biases considerably distort the empirical measurements, hindering the interpretation and comparison of results across experiments. We develop a formal characterisation and analysis of the biases upon which we analyse their causes and main factors, as well as their impact on evaluation metrics under different experimental configurations, illustrating the theoretical findings with empirical evidence. We propose two experimental design approaches that effectively neutralise such biases to a large extent. We report experiments validating our proposed experimental variants, and comparing them to alternative approaches and metrics that have been defined in the literature with similar or related purposes.     

The initial development of spelling in Spanish: From global to analytical

During a school year, samples of words written by three groups of children of successive ages were collected. Two groups of children were in first and second year of Kindergarten (4 and 5 years of age), when alphabetic rules were not taught in a systematic way. The third group was in first year of Primary School (6 years of age), and was being taught to read and spell in a systematic way. After classifying the words written by the children, seven categories of spelling were obtained, which may represent different stages in their learning process. Their analysis showed that they are related to different types of knowledge and processes, mainly phonological ones. The results show that the development of spelling in Spanish does not qualitatively differ from that of children who learn to spell in opaque writing systems. The differences mainly involve the time it takes to learn, and the rate of acquisition.