When Easy Becomes Boring and Difficult Becomes Frustrating: Disentangling the Effects of Item Difficulty Level and Person Proficiency on Learning and Motivation.

The research on electronic learning environments has evolved towards creating adaptive learning environments. In this study, the focus is on adaptive curriculum sequencing, in particular, the efficacy of an adaptive curriculum sequencing algorithm based on matching the item difficulty level to the learner’s proficiency level. We therefore explored the effect of the relative difficulty level on learning outcome and motivation. Results indicate that, for learning environments consisting of questions focusing on just one dimension and with knowledge of correct response, it does not matter whether we present easy, moderate or difficult items or whether we present the items with a random mix of difficulty levels, regarding both learning and motivation

Deciding on different hinting techniques in assessments for intelligent tutoring systems

Intelligent Tutoring Systems (ITSs) must take advantage of their high computing capabilities and capacity for information retrieval in order to provide the most effective methodologies for improving students' learning. One type of ITS provides assessments to students and some help as a hint, when they do not know how to solve a problem. Our thesis is that the type of hinting techniques used without changing the contents can influence the learning gains and aptitudes of students. We have implemented some hinting techniques as an extension to the XTutor ITS. We found that some hinting techniques can produce a signi cant increase in students' knowledge with respect to others, but the improvement and direction of the comparison depended on some other factors such as the topics to which it was applied. We conclude that proper adaptation of hinting techniques based on different information of the systems will imply better student learning gains. In addition, the results of a student survey, which includes the students' ratings of the different hinting features they interacted with, leads to high variances, which reinforce the idea of the importance of adaptation of hinting techniques in these types of systems.This work was supported in part by the MEC-CICYT Learn3 project TIN2008-05163/TSI (Spanish Ministry of Science and Education, Programa Nacional de Tecnologías de la Información y de las Comunicaciones), and the e-Madrid project S2009/TIC-1650 (Madrid Regional Community).Publicad

A framework for evolutionary systems biology

<p>Abstract</p> <p>Background</p> <p>Many difficult problems in evolutionary genomics are related to mutations that have weak effects on fitness, as the consequences of mutations with large effects are often simple to predict. Current systems biology has accumulated much data on mutations with large effects and can predict the properties of knockout mutants in some systems. However experimental methods are too insensitive to observe small effects.</p> <p>Results</p> <p>Here I propose a novel framework that brings together evolutionary theory and current systems biology approaches in order to quantify small effects of mutations and their epistatic interactions <it>in silico</it>. Central to this approach is the definition of fitness correlates that can be computed in some current systems biology models employing the rigorous algorithms that are at the core of much work in computational systems biology. The framework exploits synergies between the realism of such models and the need to understand real systems in evolutionary theory. This framework can address many longstanding topics in evolutionary biology by defining various 'levels' of the adaptive landscape. Addressed topics include the distribution of mutational effects on fitness, as well as the nature of advantageous mutations, epistasis and robustness. Combining corresponding parameter estimates with population genetics models raises the possibility of testing evolutionary hypotheses at a new level of realism.</p> <p>Conclusion</p> <p>EvoSysBio is expected to lead to a more detailed understanding of the fundamental principles of life by combining knowledge about well-known biological systems from several disciplines. This will benefit both evolutionary theory and current systems biology. Understanding robustness by analysing distributions of mutational effects and epistasis is pivotal for drug design, cancer research, responsible genetic engineering in synthetic biology and many other practical applications.</p

Supporting learning in intelligent tutoring systems with motivational strategies.

Motivation and affect detection are prominent yet challenging areas of research in the field of Intelligent Tutoring Systems (ITSs). Devising strategies to engage learners and motivate them to practice regularly are of great interest to researchers. In the learning and education domain, where students use ITSs regularly, motivating them to engage with the system effectively may lead to higher learning outcomes. Therefore, developing an ITS which provides a complete learning experience to students by catering to their cognitive, affective, metacognitive, and motivational needs is an ambitious yet promising area of research. This dissertation is the first step towards this goal in the context of SQL-Tutor, a mature ITS for tutoring SQL. In this research project, I have conducted a series of studies to detect and evaluate learners' affective states and employed various strategies for increasing motivation and engagement to improve learning from SQL-Tutor. Firstly, I established the reliability of iMotions to correctly identify learners' emotions and found that worked examples alleviated learners' frustration while solving problems with SQL-Tutor. Gamification is introduced as a motivational strategy to persuade learners to practice with the system. Gamification has emerged as a strong engagement and motivation strategy in learning environments for young learners. I evaluated the effects of gamified SQL-Tutor on undergraduate students and found that gamification indirectly improved learning by influencing learners’ time on task. It helped students by increasing their motivation which produce similar effects as intrinsically motivated students. Additionally, prior knowledge, gamification experience, and interest in the topic moderated the effects of gamification. Lastly, self-regulated learning support is presented as another strategy to affect learners’ internal motivation and skills. The support provided in the form of interventions improved students’ learning outcomes. Additionally, the learners' challenge-accepting behaviour, problem selection, goal setting, and self-reflection have improved with support without experiencing any negative emotions. This research project contributes to the latest trends of motivation and learning research in ITS

Low-overhead Online Code Transformations.

The ability to perform online code transformations - to dynamically change the implementation of running native programs - has been shown to be useful in domains as diverse as optimization, security, debugging, resilience and portability. However, conventional techniques for performing online code transformations carry significant runtime overhead, limiting their applicability for performance-sensitive applications. This dissertation proposes and investigates a novel low-overhead online code transformation technique that works by running the dynamic compiler asynchronously and in parallel to the running program. As a consequence, this technique allows programs to execute with the online code transformation capability at near-native speed, unlocking a host of additional opportunities that can take advantage of the ability to re-visit compilation choices as the program runs. This dissertation builds on the low-overhead online code transformation mechanism, describing three novel runtime systems that represent in best-in-class solutions to three challenging problems facing modern computer scientists. First, I leverage online code transformations to significantly increase the utilization of multicore datacenter servers by dynamically managing program cache contention. Compared to state-of-the-art prior work that mitigate contention by throttling application execution, the proposed technique achieves a 1.3-1.5x improvement in application performance. Second, I build a technique to automatically configure and parameterize approximate computing techniques for each program input. This technique results in the ability to configure approximate computing to achieve an average performance improvement of 10.2x while maintaining 90% result accuracy, which significantly improves over oracle versions of prior techniques. Third, I build an operating system designed to secure running applications from dynamic return oriented programming attacks by efficiently, transparently and continuously re-randomizing the code of running programs. The technique is able to re-randomize program code at a frequency of 300ms with an average overhead of 9%, a frequency fast enough to resist state-of-the-art return oriented programming attacks based on memory disclosures and side channels.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120775/1/mlaurenz_1.pd