Temperament and self-based correlates of cooperative, competitive and individualistic learning preferences

People vary in the extent to which they prefer cooperative, competitive or individualistic achievement tasks. In the present research, we conducted two studies designed to investigate correlates and possible roots of these social interdependence orientations, namely approach and avoidance temperament, general self-efficacy, implicit theories of intelligence, and contingencies of self-worth based in others’ approval, competition, and academic competence. The results indicated that approach temperament, general self-efficacy, and incremental theory were positively, and entity theory was negatively related to cooperative preferences (|r| range from .11 to .41); approach temperament, general self-efficacy, competition contingencies, and academic competence contingencies were positively related to competitive preferences (|r| range from .16 to .46); and avoidance temperament, entity theory, competitive contingencies, and academic competence contingencies were positively related, and incremental theory was negatively related to individualistic preferences (|r| range from .09 to .15). The findings are discussed with regard to the meaning of each of the three social interdependence orientations, cultural differences among the observed relations, and implications for practicioners.

Consulting young children about barriers and supports to learning

From consideration of children's rights in general and equal opportunities for disabled children in particular, it is important to consult children about barriers and supports to learning and participation. Finding appropriate and feasible ways, however, to incorporate this into educational programmes for younger children can present challenges. Here we report on what happened when teachers from reception classes in England for children aged 4–5 years implemented activities designed to access pupils' views about what helps or hinders at school. Teachers evaluated the feasibility and usefulness of the activities and, together with a small sample of children's responses, this showed that young children could indeed identify aspects of school life they like or dislike, laying the foundations for identifying barriers and supports to learning. Teachers' responses highlighted the importance of careful choice of activity to meet the needs of young children, particularly those with communication difficulties and/or low self-confidence, with staff in some cases adapting and merging activities to suit pupils' needs. Sensitive issues emerged concerning the introduction of consultation activities early in children's school careers. The implications of a compliant rather than collaborative approach by teachers are discussed in the context of children's right to have their views heard, and their developing understanding of difference.

'Rules of engagement’: how experiential learning facilitates the formation of a public–private partnership in Russia

Purpose – The purpose of this paper is to investigate to what extent one can apply experiential learning theory (ELT) to the public-private partnership (PPP) setting in Russia and to draw insights regarding the learning cycle ' s nature. Additionally, the paper assesses whether the PPP case confirms Kolb ' s ELT. Design/methodology/approach – The case study draws upon primary data which the authors collected by interviewing informants including a PPP operator ' s managers, lawyers from Russian law firms and an expert from the National PPP Centre. The authors accomplished data source triangulation in order to ensure a high degree of research validity. Findings – Experiential learning has resulted in a successful and a relatively fast PPP project launch without the concessionary framework. The lessons learned include the need for effective stakeholder engagement; avoiding being stuck in bureaucracy such as collaboration with Federal Ministries and anti-trust agency; avoiding application for government funding as the approval process is tangled and lengthy; attracting strategic private investors; shaping positive public perception of a PPP project; and making continuous efforts in order to effectively mitigate the public acceptance risk. Originality/value – The paper contributes to ELT by incorporating the impact of social environment in the learning model. Additionally, the paper tests the applicability of ELT to learning in the complex organisational setting, i.e., a PPP.

Intraindividual relations between achievement goals and discrete achievement emotions: an experience sampling approach

Theories on the link between achievement goals and achievement emotions focus on their within-person functional relationship (i.e., intraindividual relations). However, empirical studies have failed to analyze these intraindividual relations and have instead examined between-person covariation of the two constructs (i.e., interindividual relations). Aiming to better connect theory and empirical research, the present study (N = 120 10th grade students) analyzed intraindividual relations by assessing students’ state goals and emotions using experience sampling (N = 1,409 assessments within persons). In order to replicate previous findings on interindividual relations, students’ trait goals and emotions were assessed using self-report questionnaires. Despite being statistically independent, both types of relations were consistent with theoretical expectations, as shown by multi-level modeling: Mastery goals were positive predictors of enjoyment and negative predictors of boredom and anger; performance-approach goals were positive predictors of pride; and performance-avoidance goals were positive predictors of anxiety and shame. Reasons for the convergence of intra- and interindividual findings, directions for future research, and implications for educational practice are discussed.

Motivational trajectories for early language learning across the primary-secondary school transition

The transition from primary to secondary school is an area of concern across a range of curriculum subjects, and this is no less so for foreign language learning. Indeed problems with transition have been identified in England as an important barrier to the introduction of language learning to the primary school curriculum, with implications for learners’ longer-term motivation for the subject. This longitudinal study investigated, through a questionnaire, the development of 233 learners’ motivation for learning French in England, during the transition from primary to secondary schooling. It also explored whether levels and patterns of motivation differed according to the type of language teaching experienced, comparing a largely oracy-focused approach with one with greater emphasis on literacy activities. Learners showed high and increasing levels of motivation across transition, placing particular value on learning French for travel. Being taught through an oracy or a literacy-focused approach had less impact on learners’ motivation than broader classroom experiences, with the development of a sense of progress and feeling that instruction met their learning needs being especially important. A growing disjuncture emerged between valuing the learning of French for travel/communication and learners’ low levels of self-efficacy for communication with native speakers, together with a desire for more communication-based activities. By the end of the first year of secondary school less positive attitudes towards learning French and less optimism about the possibility of future progress were beginning to emerge. The paper concludes by outlining the implications of the study for classroom practice in language learning.

Using problem-based learning in a chemistry practical class for pharmacy students and engaging them with feedback

Objective: To introduce a new approach to problem based learning (PBL) used in the context of medicinal chemistry practical class teaching pharmacy students. Design: The described chemistry practical is based on independent studies by small groups of undergraduate students (4-5), who design their own practical work taking relevant professional standards into account. Students are carefully guided by feedback and acquire a set of skills important to their future profession as healthcare professionals. This model has been tailored to the application of PBL in a chemistry practical class setting for a large student cohort (150 students). Assessment: The achievement of learning outcomes is based on the submission of relevant documentation including a certificate of analysis, in addition to peer assessment. Some of the learning outcomes are also assessed in the final written examination at the end of the academic year. Conclusion: The described design of a novel PBL chemistry laboratory course for pharmacy students has been found to be successful. Self-reflective learning and engagement with feedback were encouraged, and students enjoyed the challenging learning experience. Skills that are highly essential for the students’ future careers as healthcare professionals are promoted.

Reducing defects through organizational learning within a Housing Association environment

Housing Associations (HAs) contribute circa 20% of the UK’s housing supply. HAs are however under increasing pressure as a result of funding cuts and rent reductions. Due to the increased pressure, a number of processes are currently being reviewed by HAs, especially how they manage and learn from defects. Learning from defects is considered a useful approach to achieving defect reduction within the UK housebuilding industry. This paper contributes to our understanding of how HAs learn from defects by undertaking an initial round table discussion with key HA stakeholders as part of an ongoing collaborative research project with the National House Building Council (NHBC) to better understand how house builders and HAs learn from defects to reduce their prevalence. The initial discussion shows that defect information runs through a number of groups, both internal and external of a HA during both the defects management process and organizational learning (OL) process. Furthermore, HAs are reliant on capturing and recording defect data as the foundation for the OL process. During the OL process defect data analysis is the primary enabler to recognizing a need for a change to organizational routines. When a need for change has been recognized, new options are typically pursued to design out defects via updates to a HAs Employer’s Requirements. Proposed solutions are selected by a review board and committed to organizational routine. After implementing a change, both structured and unstructured feedback is sought to establish the change’s success. The findings from the HA discussion demonstrates that OL can achieve defect reduction within the house building sector in the UK. The paper concludes by outlining a potential ‘learning from defects model’ for the housebuilding industry as well as describing future work.

The Masters in Teaching and Learning (MTL): a retrospective, intrinsic case study of the process of policy into practice and its impact on teacher participants

The research reported here is a retrospective case study of the recent (2010) introduction of the Masters in Teaching and Learning (MTL) as a post-graduate level programme of professional development for teachers. It contributes to the debate and research over the past two decades about the impact of post-graduate professional development and appropriate ways of delivering it. The study is located within an extensive body of literature dealing with the importance of the teaching profession with regard to the success of schools and pupils and the impact of professional development on teaching quality and of teaching quality on attainment. A further relevant context is the ongoing tension between the teaching profession and academics on the one hand and government and political actors on the other, in respect of the approaches to professional development and to the control of educational processes. The research questions which inform the study deal with the perspectives of various participants – policy makers, programme directors, coaches and teachers studying for the MTL – on the extent to which the MTL is likely to have an ameliorative effect on teaching and pupil attainment, their experiences of the process of policy development and their experiences as course participants. The study adopts a case study approach which involves elite interviews with those responsible for the development and implementation of the MTL, questionnaires completed by MTL course participants and a comparison group taking a conventional MA and in depth interviews with participants and coaches. The results revealed tensions and difficulties associated with the development of the MTL including uneasy relationships between HE institutions and government agencies, ideas about ‘producer capture’, the relevance of the MBA model and concern over the role of coaches. However, while acknowledging various difficulties and some misconceived expectations they viewed its potential to meet its expressed aims positively, given time. Course participants were positive about their experience of the MTL and felt that it had contributed to many aspects of their professional development. Most saw it as a positive experience despite the variable quality of support from their schools, particularly in the form of the school-based coach the concept of which had been heralded as the bellwether of the MTL. It was striking that the responses of the MTL participants were very similar to those of teachers taking a conventional MA. A finding which would repay further investigation is that while the great majority of course participants felt that the MTL (and the MA) had contributed to their becoming more effective teachers they were much less confident that it had contributed to increased pupil attainment.

A hybrid case adaptation approach for case-based reasoning

Case-Based Reasoning is a methodology for problem solving based on past experiences. This methodology tries to solve a new problem by retrieving and adapting previously known solutions of similar problems. However, retrieved solutions, in general, require adaptations in order to be applied to new contexts. One of the major challenges in Case-Based Reasoning is the development of an efficient methodology for case adaptation. The most widely used form of adaptation employs hand coded adaptation rules, which demands a significant knowledge acquisition and engineering effort. An alternative to overcome the difficulties associated with the acquisition of knowledge for case adaptation has been the use of hybrid approaches and automatic learning algorithms for the acquisition of the knowledge used for the adaptation. We investigate the use of hybrid approaches for case adaptation employing Machine Learning algorithms. The approaches investigated how to automatically learn adaptation knowledge from a case base and apply it to adapt retrieved solutions. In order to verify the potential of the proposed approaches, they are experimentally compared with individual Machine Learning techniques. The results obtained indicate the potential of these approaches as an efficient approach for acquiring case adaptation knowledge. They show that the combination of Instance-Based Learning and Inductive Learning paradigms and the use of a data set of adaptation patterns yield adaptations of the retrieved solutions with high predictive accuracy.

A hybrid approach to learn with imbalanced classes using evolutionary algorithms

There is an increasing interest in the application of Evolutionary Algorithms (EAs) to induce classification rules. This hybrid approach can benefit areas where classical methods for rule induction have not been very successful. One example is the induction of classification rules in imbalanced domains. Imbalanced data occur when one or more classes heavily outnumber other classes. Frequently, classical machine learning (ML) classifiers are not able to learn in the presence of imbalanced data sets, inducing classification models that always predict the most numerous classes. In this work, we propose a novel hybrid approach to deal with this problem. We create several balanced data sets with all minority class cases and a random sample of majority class cases. These balanced data sets are fed to classical ML systems that produce rule sets. The rule sets are combined creating a pool of rules and an EA is used to build a classifier from this pool of rules. This hybrid approach has some advantages over undersampling, since it reduces the amount of discarded information, and some advantages over oversampling, since it avoids overfitting. The proposed approach was experimentally analysed and the experimental results show an improvement in the classification performance measured as the area under the receiver operating characteristics (ROC) curve.

A Road Following Approach Using Artificial Neural Networks Combinations

Navigation is a broad topic that has been receiving considerable attention from the mobile robotic community over the years. In order to execute autonomous driving in outdoor urban environments it is necessary to identify parts of the terrain that can be traversed and parts that should be avoided. This paper describes an analyses of terrain identification based on different visual information using a MLP artificial neural network and combining responses of many classifiers. Experimental tests using a vehicle and a video camera have been conducted in real scenarios to evaluate the proposed approach.

Evolutionary model trees for handling continuous classes in machine learning

Model trees are a particular case of decision trees employed to solve regression problems. They have the advantage of presenting an interpretable output, helping the end-user to get more confidence in the prediction and providing the basis for the end-user to have new insight about the data, confirming or rejecting hypotheses previously formed. Moreover, model trees present an acceptable level of predictive performance in comparison to most techniques used for solving regression problems. Since generating the optimal model tree is an NP-Complete problem, traditional model tree induction algorithms make use of a greedy top-down divide-and-conquer strategy, which may not converge to the global optimal solution. In this paper, we propose a novel algorithm based on the use of the evolutionary algorithms paradigm as an alternate heuristic to generate model trees in order to improve the convergence to globally near-optimal solutions. We call our new approach evolutionary model tree induction (E-Motion). We test its predictive performance using public UCI data sets, and we compare the results to traditional greedy regression/model trees induction algorithms, as well as to other evolutionary approaches. Results show that our method presents a good trade-off between predictive performance and model comprehensibility, which may be crucial in many machine learning applications. (C) 2010 Elsevier Inc. All rights reserved.

Opinion dynamics of learning agents: does seeking consensus lead to disagreement?

We study opinion dynamics in a population of interacting adaptive agents voting on a set of issues represented by vectors. We consider agents who can classify issues into one of two categories and can arrive at their opinions using an adaptive algorithm. Adaptation comes from learning and the information for the learning process comes from interacting with other neighboring agents and trying to change the internal state in order to concur with their opinions. The change in the internal state is driven by the information contained in the issue and in the opinion of the other agent. We present results in a simple yet rich context where each agent uses a Boolean perceptron to state their opinion. If the update occurs with information asynchronously exchanged among pairs of agents, then the typical case, if the number of issues is kept small, is the evolution into a society torn by the emergence of factions with extreme opposite beliefs. This occurs even when seeking consensus with agents with opposite opinions. If the number of issues is large, the dynamics becomes trapped, the society does not evolve into factions and a distribution of moderate opinions is observed. The synchronous case is technically simpler and is studied by formulating the problem in terms of differential equations that describe the evolution of order parameters that measure the consensus between pairs of agents. We show that for a large number of issues and unidirectional information flow, global consensus is a fixed point; however, the approach to this consensus is glassy for large societies.

Cross-situational learning of object-word mapping using Neural Modeling Fields

The issue of how children learn the meaning of words is fundamental to developmental psychology. The recent attempts to develop or evolve efficient communication protocols among interacting robots or Virtual agents have brought that issue to a central place in more applied research fields, such as computational linguistics and neural networks, as well. An attractive approach to learning an object-word mapping is the so-called cross-situational learning. This learning scenario is based on the intuitive notion that a learner can determine the meaning of a word by finding something in common across all observed uses of that word. Here we show how the deterministic Neural Modeling Fields (NMF) categorization mechanism can be used by the learner as an efficient algorithm to infer the correct object-word mapping. To achieve that we first reduce the original on-line learning problem to a batch learning problem where the inputs to the NMF mechanism are all possible object-word associations that Could be inferred from the cross-situational learning scenario. Since many of those associations are incorrect, they are considered as clutter or noise and discarded automatically by a clutter detector model included in our NMF implementation. With these two key ingredients - batch learning and clutter detection - the NMF mechanism was capable to infer perfectly the correct object-word mapping. (C) 2009 Elsevier Ltd. All rights reserved.

Have Biofuel, Will Travel: A Colorful Experiment and a Different Approach To Teach the Undergraduate Laboratory

The substitution of petroleum-based fuels with those from renewable sources has gained momentum worldwide. A UV-vis experiment for the quantitative analysis of biofuels (bioethanol or biodiesel) in (petroleum-based) diesel oil has been developed. Before the experiment, students were given a quiz on biofuels, and then they were asked to suggest a suitable UV-vis experiment for the quantification of biofuels in diesel oil. After discussing the results of the quiz, the experiment was conducted. This included the determination of lambda(max) of the medium-dependent, that is, solvatochromic, visible absorption band of the probe 2,6-bis[4-(tert-butyl)phenyl]-4-{2,4,6-tris[4-(tert-butyl)phenyl]pyridinium-1-yl}phenolate as a function of fuel composition. The students appreciated that the subject was linked to a daily situation and that they were asked to suggest the experiment. This experiment served to introduce the phenomena of solvation and solvatochromism.