Success and failure in school mathematics: effects of instruction and school environment

Given the stubborn phenomenon of many children's serious difficulties and failure in mathematical learning, the hypothesis of developmental delay, or neurocognitively based deficiency should be complemented by further explanantions of children's weaknesses and substandard performance in mathematics. One obvious explanantion is that schooling and instruction for low ability children and for children with special needs is often inadequate. The present contribution examines selected research on mathematics learning under a cognitive instructional (didactical) perspective. Constructivist learning theory, the rooting of meaningful learning in concrete modeling activities, the balancing of understanding and practice in mathematics instruction, diagnostic and adaptive teaching, computer-assisted instruction, and the role of nonmathematical stumbling-blocks are discussed as principles and factors of effective mathematics learning and teachin

Examining the Impact of Error Encouragement on Training Outcomes

Error management training has been praised as an effective strategy for facilitating adaptive transfer. However, potential variations have not yet been examined to determine if an alternative format may be equally or more effective. As standard practice, error-related instructions in error management training encourage learners to make errors and to view these errors as learning opportunities. Also, an overwhelming majority of research on this topic has focused learner development of procedural computer software skills. The empirical literature provides little guidance in terms of the boundaries within which error management training is an effective training approach. The purpose of this research was to examine the relative effectiveness of a modified error management training approach for influencing adaptive transfer in contrast to both standard error management training and error avoidant training. The modified error management approach encouraged learners to do their best to avoid errors, but maintained traditional instructions to learn from errors. The effectiveness of these three training conditions for promoting adaptive transfer was examined in two studies. The first study applied the error strategies to a complex decision-making task, and the second study compared the strategies relative effectiveness for a fine motor skills task. Study 1 results indicated that both error management training approaches were associated with higher adaptive learning compared to an error avoidant training approach. Error management and the modified error management did not significantly differ. In Study 2, error management training and error avoidant training both demonstrated greater adaptive transfer than did the modified approach. The mediating roles of metacognition and emotion regulation were examined, but unsupported, in both studies. Implications for future research and organizational practice are discussed

The role of ICTs' in field supervision of undergraduate students at Makerere University: an activity theory system perspective

This research investigates how Information and Communication Technologies (ICT) tools mediate in field supervision of undergraduate students. The research used Activity Theory systems to show that good supervisory practices lead to expansive learning. The study conducted over a two year period of eight weeks each, focussed on nine supervisors, students and administrators in the international programme (summer for the Western Countries) is organised by the College of Veterinary Medicine and Bio-Security of Makerere University. The students undertake field attachment and are supervised using various ICT tools. The research used qualitative methods and was grounded in Activity Theory. Data was collected through interviews, their participation and discussion in the Learning Management Systems (LMS) and the social media network (Facebook & Diigo) and through various feedback reports either from the supervisors or from the students to collect as much information as possible so as to understand the role ICT plays in this process. The research found that while ICT tools mediate in field supervision of undergraduate students through aggregation of multiple experiences and by providing a virtual proximity in the supervisory process. It also found that there are barriers in its usage which need to be addressed when doing so. These included; internet access and availability as key, power outages, and technical knowhow were also mentioned. The research further found that lack of adequate ICT tools to be used in the field, skills and at times failure to credit the source of content hindered its effectiveness. This inevitably creates lack of consistence in the way they are used. The research, therefore, concludes that there is need for a holistic approach to address the problem of barriers and usage so as to have a comprehensive implementation plan for the use of ICT in the supervisory process. This will assist supervisors in integrating them in their practice

A Classification of Motivation and Behavior Change Techniques Used in Self-Determination Theory-Based Interventions in Health Contexts

While evidence suggests that interventions based on self-determination theory have efficacy in motivating adoption and maintenance of health-related behaviors, and in promoting adaptive psychological outcomes, the motivational techniques that comprise the content of these interventions have not been comprehensively identified or described. The aim of the present study was to develop a classification system of the techniques that comprise self-determination theory interventions, with satisfaction of psychological needs as an organizing principle. Candidate techniques were identified through a comprehensive review of self-determination theory interventions and nomination by experts. The study team developed a preliminary list of candidate techniques accompanied by labels, definitions, and function descriptions of each. Each technique was aligned with the most closely-related psychological need satisfaction construct (autonomy, competence, or relatedness). Using an iterative expert consensus procedure, participating experts (N=18) judged each technique on the preliminary list for redundancy, essentiality, uniqueness, and the proposed link between the technique and basic psychological need. The procedure produced a final classification of 21 motivation and behavior change techniques (MBCTs). Redundancies between final MBCTs against techniques from existing behavior change technique taxonomies were also checked. The classification system is the first formal attempt to systematize self-determination theory intervention techniques. The classification is expected to enhance consistency in descriptions of selfdetermination theory-based interventions in health contexts, and assist in facilitating synthesis of evidence on interventions based on the theory. The classification is also expected to guide future efforts to identify, describe, and classify the techniques that comprise self-determination theory-based interventions in multiple domains

Factors Affecting Knowledge Sharing in Virtual Learning Teams (VLTs) in Distance Education

This study asserts that knowledge sharing (a component of knowledge management) in distance education virtual learning teams (VLTs) is important for successful collaborative learning and that various factors characterizing person and environment can impact VLT members\u27 knowledge sharing behavior. Factors under the category of person are VLT members\u27 competencies for working on VLTs, and their learning goal orientation and performance goal orientation. Factors under VLT environment are social presence in the VLT, the VLT learning community, satisfaction with the VLT, task type, and instructor strategies. Knowledge sharing is defined as a behavior in which VLT members impart their expertise, insight, or understanding to other members in the VLT or to the entire team, intending for the recipients to have that knowledge in common with themselves, the sharers. The study used Bandura\u27s (1986) model of triadic reciprocal causation as a theoretical framework. The model is suitable for this research because it considers relationships between person, environment, and behavior. First, the study identified variables that are directly related to knowledge sharing. Next, the study validated those constructs. After the constructs had been validated, they were entered into a knowledge sharing measurement model. The study empirically tested a measurement model with five latent variables, taking into account the measurement error. Next, the study cross-validated the model with multiple groups drawn from the same sample. The sample consisted of data from 1,374 participants matriculated in graduate and undergraduate programs at an online university. The data were analyzed using split sample methodology, multiple regression analysis, and structural equation modeling techniques (factor analysis and latent variable structural equation modeling- SEM). The study\u27s findings suggest that there is a direct predictive relationship between knowledge sharing and competencies for working on VLTs, learning environment, social presence, task type, and mediating relationships for learning community, social presence, and task type in the knowledge sharing model. This study contributes to research, theory, and practice. It concludes by presenting a knowledge sharing model that can be reevaluated with distance education student populations at various kinds of distance education institutions

Transferring motivation from educational to extramural contexts: A review of the trans-contextual model

A key question for educators is whether teaching styles, methods, and practices not only foster motivation toward, and persistence with, learning activities in the classroom but also in contexts outside of school (Ciani et al. 2010). There is a wealth of evidence in the social psychological literature applied to educational contexts that has indicated that teaching styles and other motivational strategies adopted by social agents like teachers and educators lead to adaptive outcomes within the school context. For example, adopting democratic teaching styles (Tomasetto 2004), fostering mastery oriented motivational climates (Barkoukis et al. 2008), and providing autonomy support (Reeve 2002) are strategies that have been utilised by social agents in educational contexts to promote increased motivation among pupils and students. Overall, the support offered by teachers in the classroom has been shown to have direct effects on pupils’ emotional and motivational responses (e.g. Covington and Dray 2002). Furthermore, the adoption of autonomy-supportive strategies has been associated with numerous adaptive outcomes such as academic achievement (Deci et al. 1991), perceived competence (Harter 1985), deep learning of concepts (Lau et al.2008), and selection of tasks of optimal challenge (Murphy and Thomas 2008). There is also some evidence that such strategies also foster desirable outcomes beyond the classroom, such as engagement in extra-curricular activities (Tomasetto 2004) and studying behaviour (Kolic-Vehovec et al. 2008). This indicates that social agents’ behaviours in educational settings may motivate students to engage in behaviours and activities outside of school that are adaptive in terms of learning and skill development. Such influences likely fulfil a key goal of education to influence educational activities beyond the classroom.In addition, motivating students outside of the classroom will meet educational aims to promote increased transformative experiences (Pugh et al. 2010) and inquisitive behaviours (Yoon 2009) among pupils that assist in the development of flexible, critical, and analytic thinking skills that are generalizable and transferable. It must, however, be stressed that little is known of the processes by which teacher behaviours in educational contexts impact on students motivation and behaviour within the school and, most importantly, outside school. The aim of the present review is to provide an overview of a recently developed motivational model that outlines the processes by which perceptions of social agents’ behaviours that support motivation and learning affect motivation to engage in educational activities in both the classroom and extramural contexts. The model is based on the integration of leading social psychological and motivational theories and not only identifies the important factors and processes involved in trans-contextual motivation, but also provides an impetus for the development of interventions to promote motivation for learning activities in both educational and extramural contexts. After outlining the conceptual and theoretical bases of the model, we review a series of prospective and intervention studies from our laboratory that provides evidence to support its core trans-contextual premises. We also outline how the model serves as a novel basis for educational interventions to enhance motivation among pupils in educational and extramural contexts and the potential of the model to be applied to interventions in diverse educational contexts to promote general educational aims of fostering adaptive outcomes in students outside the classroom

A data-assisted approach to supporting instructional interventions in technology enhanced learning environments

The design of intelligent learning environments requires significant up-front resources and expertise. These environments generally maintain complex and comprehensive knowledge bases describing pedagogical approaches, learner traits, and content models. This has limited the influence of these technologies in higher education, which instead largely uses learning content management systems in order to deliver non-classroom instruction to learners. This dissertation puts forth a data-assisted approach to embedding intelligence within learning environments. In this approach, instructional experts are provided with summaries of the activities of learners who interact with technology enhanced learning tools. These experts, which may include instructors, instructional designers, educational technologists, and others, use this data to gain insight into the activities of their learners. These insights lead experts to form instructional interventions which can be used to enhance the learning experience. The novel aspect of this approach is that the actions of the intelligent learning environment are now not just those of the learners and software constructs, but also those of the educational experts who may be supporting the learning process. The kinds of insights and interventions that come from application of the data-assisted approach vary with the domain being taught, the epistemology and pedagogical techniques being employed, and the particulars of the cohort being instructed. In this dissertation, three investigations using the data-assisted approach are described. The first of these demonstrates the effects of making available to instructors novel sociogram-based visualizations of online asynchronous discourse. By making instructors aware of the discussion habits of both themselves and learners, the instructors are better able to measure the effect of their teaching practice. This enables them to change their activities in response to the social networks that form between their learners, allowing them to react to deficiencies in the learning environment. Through these visualizations it is demonstrated that instructors can effectively change their pedagogy based on seeing data of their students’ interactions. The second investigation described in this dissertation is the application of unsupervised machine learning to the viewing habits of learners using lecture capture facilities. By clustering learners into groups based on behaviour and correlating groups with academic outcome, a model of positive learning activity can be described. This is particularly useful for instructional designers who are evaluating the role of learning technologies in programs as it contextualizes how technologies enable success in learners. Through this investigation it is demonstrated that the viewership data of learners can be used to assist designers in building higher level models of learning that can be used for evaluating the use of specific tools in blended learning situations. Finally, the results of applying supervised machine learning to the indexing of lecture video is described. Usage data collected from software is increasingly being used by software engineers to make technologies that are more customizable and adaptable. In this dissertation, it is demonstrated that supervised machine learning can provide human-like indexing of lecture videos that is more accurate than current techniques. Further, these indices can be customized for groups of learners, increasing the level of personalization in the learning environment. This investigation demonstrates that the data-assisted approach can also be used by application developers who are building software features for personalization into intelligent learning environments. Through this work, it is shown that a data-assisted approach to supporting instructional interventions in technology enhanced learning environments is both possible and can positively impact the teaching and learning process. By making available to instructional experts the online activities of learners, experts can better understand and react to patterns of use that develop, making for a more effective and personalized learning environment. This approach differs from traditional methods of building intelligent learning environments, which apply learning theories a priori to instructional design, and do not leverage the in situ data collected about learners