Functional Baby Talk: Analysis of Code Fragments from Novice Haskell Programmers

What kinds of mistakes are made by novice Haskell developers, as they learn about functional programming? Is it possible to analyze these errors in order to improve the pedagogy of Haskell? In 2016, we delivered a massive open online course which featured an interactive code evaluation environment. We captured and analyzed 161K interactions from learners. We report typical novice developer behavior; for instance, the mean time spent on an interactive tutorial is around eight minutes. Although our environment was restricted, we gain some understanding of Haskell novice errors. Parenthesis mismatches, lexical scoping errors and do block misunderstandings are common. Finally, we make recommendations about how such beginner code evaluation environments might be enhanced

AM-OER: An Agile Method for the Development of Open Educational Resources

Open Educational Resources have emerged as important elements of education in the contemporary society, promoting life-long and personalized learning that transcends social, eco- nomic and geographical barriers. To achieve the potential of OERs and bring impact on education, it is necessary to increase their development and supply. However, one of the current challenges is how to produce quality and relevant OERs to be reused and adapted to different contexts and learning situations. In this paper we proposed an agile method for the development of OERs – AM-OER, grounded on agile practices from Software Engineering. Learning Design practices from the OULDI project (UK Open University) are also embedded into the AM-OER aiming at improving quality and facilitating reuse and adaptation of OERs. In order to validate AM-OER, an experiment was conducted by applying it in the development of an OER on software testing. The results showed preliminary evidences on the applicability, effectiveness and ef ciency of the method in the development of OERs

The future of technology enhanced active learning – a roadmap

The notion of active learning refers to the active involvement of learner in the learning process, capturing ideas of learning-by-doing and the fact that active participation and knowledge construction leads to deeper and more sustained learning. Interactivity, in particular learnercontent interaction, is a central aspect of technology-enhanced active learning. In this roadmap, the pedagogical background is discussed, the essential dimensions of technology-enhanced active learning systems are outlined and the factors that are expected to influence these systems currently and in the future are identified. A central aim is to address this promising field from a best practices perspective, clarifying central issues and formulating an agenda for future developments in the form of a roadmap

A Unified Framework of Constrained Regression

Generalized additive models (GAMs) play an important role in modeling and understanding complex relationships in modern applied statistics. They allow for flexible, data-driven estimation of covariate effects. Yet researchers often have a priori knowledge of certain effects, which might be monotonic or periodic (cyclic) or should fulfill boundary conditions. We propose a unified framework to incorporate these constraints for both univariate and bivariate effect estimates and for varying coefficients. As the framework is based on component-wise boosting methods, variables can be selected intrinsically, and effects can be estimated for a wide range of different distributional assumptions. Bootstrap confidence intervals for the effect estimates are derived to assess the models. We present three case studies from environmental sciences to illustrate the proposed seamless modeling framework. All discussed constrained effect estimates are implemented in the comprehensive R package mboost for model-based boosting.Comment: This is a preliminary version of the manuscript. The final publication is available at http://link.springer.com/article/10.1007/s11222-014-9520-

Designing Engaging Learning Experiences in Programming

In this paper we describe work to investigate the creation of engaging programming learning experiences. Background research informed the design of four fieldwork studies to explore how programming tasks could be framed to motivate learners. Our empirical findings from these four field studies are summarized here, with a particular focus upon one – Whack a Mole – which compared the use of a physical interface with the use of a screen-based equivalent interface to obtain insights into what made for an engaging learning experience. Emotions reported by two sets of participant undergraduate students were analyzed, identifying the links between the emotions experienced during programming and their origin. Evidence was collected of the very positive emotions experienced by learners programming with a physical interface (Arduino) in comparison with a similar program developed using a screen-based equivalent interface. A follow-up study provided further evidence of the motivation of personalized design of programming tangible physical artefacts. Collating all the evidence led to the design of a set of ‘Learning Dimensions’ which may provide educators with insights to support key design decisions for the creation of engaging programming learning experiences

Identifying Risk Factors for Severe Childhood Malnutrition by Boosting Additive Quantile Regression

Ordinary linear and generalized linear regression models relate the mean of a response variable to a linear combination of covariate effects and, as a consequence, focus on average properties of the response. Analyzing childhood malnutrition in developing or transition countries based on such a regression model implies that the estimated effects describe the average nutritional status. However, it is of even larger interest to analyze quantiles of the response distribution such as the 5% or 10% quantile that relate to the risk of children for extreme malnutrition. In this paper, we analyze data on childhood malnutrition collected in the 2005/2006 India Demographic and Health Survey based on a semiparametric extension of quantile regression models where nonlinear effects are included in the model equation, leading to additive quantile regression. The variable selection and model choice problems associated with estimating an additive quantile regression model are addressed by a novel boosting approach. Based on this rather general class of statistical learning procedures for empirical risk minimization, we develop, evaluate and apply a boosting algorithm for quantile regression. Our proposal allows for data-driven determination of the amount of smoothness required for the nonlinear effects and combines model selection with an automatic variable selection property. The results of our empirical evaluation suggest that boosting is an appropriate tool for estimation in linear and additive quantile regression models and helps to identify yet unknown risk factors for childhood malnutrition

Evaluation of e-learning web sites using fuzzy axiomatic design based approach

High quality web site has been generally recognized as a critical enabler to conduct online business. Numerous studies exist in the literature to measure the business performance in relation to web site quality. In this paper, an axiomatic design based approach for fuzzy group decision making is adopted to evaluate the quality of e-learning web sites. Another multi-criteria decision making technique, namely fuzzy TOPSIS, is applied in order to validate the outcome. The methodology proposed in this paper has the advantage of incorporating requirements and enabling reductions in the problem size, as compared to fuzzy TOPSIS. A case study focusing on Turkish e-learning websites is presented, and based on the empirical findings, managerial implications and recommendations for future research are offered