Motivating learners through information literacy

This paper is included in the Information Literacy: Key to an Inclusive Society, the proceedings of the European Conference on Information Literacy, 2016. The paper introduces a model for creating information literacy learning activities that motivate students. The model draws from informed learning, an approach to information literacy that emphasizes the role that information plays in fostering learning about a subject. Self-determination theory, a motivational theory that focuses on enabling self-determined learners, is applied within the informed learning framework. The results of the investigation outline characteristics of motivating learning activities that enable learning subject content through engagement with information. The model is intended to be used by librarians when working with classroom teachers to foster greater student learning gains through creative and reflective engagement with information

Cognitive engagement in the problem-based learning classroom

The objective of the present study was to examine to what extent autonomy in problem-based learning (PBL) results in cognitive engagement with the topic at hand. To that end, a short self-report instrument was devised and validated. Moreover, it was examined how cognitive engagement develops as a function of the learning process and the extent to which cognitive engagement determines subsequent levels of cognitive engagement during a one-day PBL event. Data were analyzed by means of confirmatory factor analysis, repeated measures ANOVA, and path analysis. The results showed that the new measure of situational cognitive engagement is valid and reliable. Furthermore, the results revealed that students’ cognitive engagement significantly increased as a function of the learning event. Implications of these findings for PBL are discussed

The effects of strength-based versus deficit-based self-regulated learning strategies on students' effort intentions

In two randomized experiments, one conducted online (n = 174) and one in the classroom (n = 267), we tested the effects of two types of self-regulated learning (SRL) strategies on students’ intentions to put effort into professional development activities: strength-based SRL strategies (i.e., identifying perceived relative strengths and, subsequently, selecting professional development activities to further improve those strengths) versus deficit-based SRL strategies (i.e., identifying perceived relative short- comings and, subsequently, selecting professional develop- ment activities to improve those shortcomings). Across both studies, analysis of variance revealed that, relative to students who used deficit-based SRL strategies, students who used strength-based SRL strategies were higher in perceived competence, intrinsic motivation, and effort in- tentions. Moreover, the results of multi-mediator analysis and structural equation modeling supported the hypothesis that the effect of strength-based versus deficit-based SRL strategies on students’ effort intentions was sequentially mediated by perceived competence and intrinsic motiva- tion. Implications for the application of self-regulated learning strategies in the context of professional self-de- velopment are discussed

Determinants of quality, latency, and amount of Stack Overflow answers about recent Android APIs

Stack Overflow is a popular crowdsourced question and answer website for programming-related issues. It is an invaluable resource for software developers; on average, questions posted there get answered in minutes to an hour. Questions about well established topics, e.g., the coercion operator in C++, or the difference between canonical and class names in Java, get asked often in one form or another, and answered very quickly. On the other hand, questions on previously unseen or niche topics take a while to get a good answer. This is particularly the case with questions about current updates to or the introduction of new application programming interfaces (APIs). In a hyper-competitive online market, getting good answers to current programming questions sooner could increase the chances of an app getting released and used. So, can developers anyhow, e.g., hasten the speed to good answers to questions about new APIs? Here, we empirically study Stack Overflow questions pertaining to new Android APIs and their associated answers. We contrast the interest in these questions, their answer quality, and timeliness of their answers to questions about old APIs. We find that Stack Overflow answerers in general prioritize with respect to currentness: questions about new APIs do get more answers, but good quality answers take longer. We also find that incentives in terms of question bounties, if used appropriately, can significantly shorten the time and increase answer quality. Interestingly, no operationalization of bounty amount shows significance in our models. In practice, our findings confirm the value of bounties in enhancing expert participation. In addition, they show that the Stack Overflow style of crowdsourcing, for all its glory in providing answers about established programming knowledge, is less effective with new API questions