Learning from a deceptively spacious policy discourse

Networked Learning, e-Learning and Technology Enhanced Learning have each been defined in different ways, as people's understanding about technology in education has developed. Yet each could also be considered as a terminology competing for a contested conceptual space. Theoretically this can be a ‘fertile trans-disciplinary ground for represented disciplines to affect and potentially be re-orientated by others’ (Parchoma and Keefer, 2012), as differing perspectives on terminology and subject disciplines yield new understandings. Yet when used in government policy texts to describe connections between humans, learning and technology, terms tend to become fixed in less fertile positions linguistically. A deceptively spacious policy discourse that suggests people are free to make choices conceals an economically-based assumption that implementing new technologies, in themselves, determines learning. Yet it actually narrows choices open to people as one route is repeatedly in the foreground and humans are not visibly involved in it. An impression that the effective use of technology for endless improvement is inevitable cuts off critical social interactions and new knowledge for multiple understandings of technology in people's lives. This paper explores some findings from a corpus-based Critical Discourse Analysis of UK policy for educational technology during the last 15 years, to help to illuminate the choices made. This is important when through political economy, hierarchical or dominant neoliberal logic promotes a single ‘universal model’ of technology in education, without reference to a wider social context (Rustin, 2013). Discourse matters, because it can ‘mould identities’ (Massey, 2013) in narrow, objective economically-based terms which 'colonise discourses of democracy and student-centredness' (Greener and Perriton, 2005:67). This undermines subjective social, political, material and relational (Jones, 2012: 3) contexts for those learning when humans are omitted. Critically confronting these structures is not considered a negative activity. Whilst deterministic discourse for educational technology may leave people unconsciously restricted, I argue that, through a close analysis, it offers a deceptively spacious theoretical tool for debate about the wider social and economic context of educational technology. Methodologically it provides insights about ways technology, language and learning intersect across disciplinary borders (Giroux, 1992), as powerful, mutually constitutive elements, ever-present in networked learning situations. In sharing a replicable approach for linguistic analysis of policy discourse I hope to contribute to visions others have for a broader theoretical underpinning for educational technology, as a developing field of networked knowledge and research (Conole and Oliver, 2002; Andrews, 2011).

A novel adaptive weight selection algorithm for multi-objective multi-agent reinforcement learning

To solve multi-objective problems, multiple reward signals are often scalarized into a single value and further processed using established single-objective problem solving techniques. While the field of multi-objective optimization has made many advances in applying scalarization techniques to obtain good solution trade-offs, the utility of applying these techniques in the multi-objective multi-agent learning domain has not yet been thoroughly investigated. Agents learn the value of their decisions by linearly scalarizing their reward signals at the local level, while acceptable system wide behaviour results. However, the non-linear relationship between weighting parameters of the scalarization function and the learned policy makes the discovery of system wide trade-offs time consuming. Our first contribution is a thorough analysis of well known scalarization schemes within the multi-objective multi-agent reinforcement learning setup. The analysed approaches intelligently explore the weight-space in order to find a wider range of system trade-offs. In our second contribution, we propose a novel adaptive weight algorithm which interacts with the underlying local multi-objective solvers and allows for a better coverage of the Pareto front. Our third contribution is the experimental validation of our approach by learning bi-objective policies in self-organising smart camera networks. We note that our algorithm (i) explores the objective space faster on many problem instances, (ii) obtained solutions that exhibit a larger hypervolume, while (iii) acquiring a greater spread in the objective space.

A Taxonomy of Big Data for Optimal Predictive Machine Learning and Data Mining

Big data comes in various ways, types, shapes, forms and sizes. Indeed, almost all areas of science, technology, medicine, public health, economics, business, linguistics and social science are bombarded by ever increasing flows of data begging to be analyzed efficiently and effectively. In this paper, we propose a rough idea of a possible taxonomy of big data, along with some of the most commonly used tools for handling each particular category of bigness. The dimensionality p of the input space and the sample size n are usually the main ingredients in the characterization of data bigness. The specific statistical machine learning technique used to handle a particular big data set will depend on which category it falls in within the bigness taxonomy. Large p small n data sets for instance require a different set of tools from the large n small p variety. Among other tools, we discuss Preprocessing, Standardization, Imputation, Projection, Regularization, Penalization, Compression, Reduction, Selection, Kernelization, Hybridization, Parallelization, Aggregation, Randomization, Replication, Sequentialization. Indeed, it is important to emphasize right away that the so-called no free lunch theorem applies here, in the sense that there is no universally superior method that outperforms all other methods on all categories of bigness. It is also important to stress the fact that simplicity in the sense of Ockham’s razor non-plurality principle of parsimony tends to reign supreme when it comes to massive data. We conclude with a comparison of the predictive performance of some of the most commonly used methods on a few data sets.

Creative thinking experimentations for entrepreneurship with a disruptive, personalised and mobile game-based learning ecosystem

Based on an unprecedented need of stimulating creative capacities towards entrepreneurship to university students and young researchers, this paper introduces and analyses a smart learning ecosystem for encouraging teaching and learning on creative thinking as a distinct feature to be taught and learnt in universities. The paper introduces a mashed-up authoring architecture for designing lesson-plans and games with visual learning mechanics for creativity learning. The design process is facilitated by creativity pathways discerned across components. Participatory learning, networking and capacity building is a key aspect of the architecture, extending the learning experience and context from the classroom to outdoor (co-authoring of creative pathways by students, teachers and real-world entrepreneurs) and personal spaces. We anticipate that the smart learning ecosystem will be empirically evaluated and validated in future iterations for exploring the benefits of using games for enhancing creative mindsets, unlocking the imagination that lies within, practiced and transferred to multiple academic tribes and territories.

Improved data visualisation through multiple dissimilarity modelling

Popular dimension reduction and visualisation algorithms rely on the assumption that input dissimilarities are typically Euclidean, for instance Metric Multidimensional Scaling, t-distributed Stochastic Neighbour Embedding and the Gaussian Process Latent Variable Model. It is well known that this assumption does not hold for most datasets and often high-dimensional data sits upon a manifold of unknown global geometry. We present a method for improving the manifold charting process, coupled with Elastic MDS, such that we no longer assume that the manifold is Euclidean, or of any particular structure. We draw on the benefits of different dissimilarity measures allowing for the relative responsibilities, under a linear combination, to drive the visualisation process.

The multiple hierarchical legislatures in representative democracy

Multiple hierarchical models of representative democracies in which, for instance, voters elect county representatives, county representatives elect district representatives, district representatives elect state representatives and state representatives a president, reduces the number of electors a representative is answerable for, and therefore, considering each level separately, these models could come closer to direct democracy. In this paper we show that worst case policy bias increases with the number of hierarchical levels. This also means that the opportunities of a gerrymanderer increase in the number of hierarchical levels.

The Multiple Hierarchical Legislatures in a Representative Democracy: Districting for Policy Implementation

We build a multiple hierarchical model of a representative democracy in which, for instance, voters elect county representatives, county representatives elect district representatives, district representatives elect state representatives, and state representatives elect a prime minister. We use our model to show that the policy determined by the final representative can become more extreme as the number of hierarchical levels increases because of increased opportunities for gerrymandering. Thus, a sufficiently large number of voters gives a district maker an advantage, enabling her to implement her favorite policy. We also show that the range of implementable policies increases with the depth of the hierarchical system. Consequently, districting by a candidate in a hierarchical legislative system can be viewed as a type of policy implementation device.

Knowledge discovery using multiple sources of biological data

The primary aim of this dissertation is to develop data mining tools for knowledge discovery in biomedical data when multiple (homogeneous or heterogeneous) sources of data are available. The central hypothesis is that, when information from multiple sources of data are used appropriately and effectively, knowledge discovery can be better achieved than what is possible from only a single source. ^ Recent advances in high-throughput technology have enabled biomedical researchers to generate large volumes of diverse types of data on a genome-wide scale. These data include DNA sequences, gene expression measurements, and much more; they provide the motivation for building analysis tools to elucidate the modular organization of the cell. The challenges include efficiently and accurately extracting information from the multiple data sources; representing the information effectively, developing analytical tools, and interpreting the results in the context of the domain. ^ The first part considers the application of feature-level integration to design classifiers that discriminate between soil types. The machine learning tools, SVM and KNN, were used to successfully distinguish between several soil samples. ^ The second part considers clustering using multiple heterogeneous data sources. The resulting Multi-Source Clustering (MSC) algorithm was shown to have a better performance than clustering methods that use only a single data source or a simple feature-level integration of heterogeneous data sources. ^ The third part proposes a new approach to effectively incorporate incomplete data into clustering analysis. Adapted from K-means algorithm, the Generalized Constrained Clustering (GCC) algorithm makes use of incomplete data in the form of constraints to perform exploratory analysis. Novel approaches for extracting constraints were proposed. For sufficiently large constraint sets, the GCC algorithm outperformed the MSC algorithm. ^ The last part considers the problem of providing a theme-specific environment for mining multi-source biomedical data. The database called PlasmoTFBM, focusing on gene regulation of Plasmodium falciparum, contains diverse information and has a simple interface to allow biologists to explore the data. It provided a framework for comparing different analytical tools for predicting regulatory elements and for designing useful data mining tools. ^ The conclusion is that the experiments reported in this dissertation strongly support the central hypothesis.^

Best practices in exemplary service delivery: A multiple case study of service quality

The purpose of this study was to explain how exemplary service providers in luxury hotels provide consistently excellent service. Using a case study framework, the study investigated the service provider's strategies and concepts of service delivery, the importance and implementation of organizational and individual controls, and the role of training and learning. The study identified barriers to service provision and characteristics of the exemplary individuals that affect their ability to deliver luxury service. This study sought to better understand how exemplary service providers learn, think about, and do their work. The sample population of three Five-Diamond-Award winning resorts was selected for their potential for learning about the phenomenon of interest. The results demonstrate that exemplary service providers possess individual characteristics that are enhanced by the organizations for which they work. Exemplary service providers are often exemplary communicators who are emotionally generous and genuinely enjoy helping and serving others. Exemplary service organizations treat their employees as they treat their customers, as suggested by the Service-Profit Chain (Heskett, Sasser & Schlesinger, 1997). Further, they have systems and standards to guarantee satisfactory service experiences for every guest. They also encourage their service providers to personalize their service delivery and to seek opportunities to delight their guests, using a combination of controls, traditions and cultural values. Several customer service theories are discussed in relationship to whether they were or were not supported by the data. The study concluded that the delivery of exemplary service is a complex phenomenon that requires successful interactions between guests, service providers and the organization. A Model of Exemplary Service Delivery is presented and discussed that demonstrates the components of service quality as shown in the data. The model can be used by practitioners seeking to create, enhance, or evaluate their service quality, and by researchers seeking insights into the complex concepts in service quality research. Implications for future research are discussed.

Effects of repeated readings on reading abilities of English language learners with specific learning disabilities

This study investigated the effects of repeated readings on the reading abilities of 4, third-, fourth-, and fifth-grade English language learners (ELLs) with specific learning disabilities (SLD). A multiple baseline probe design across subjects was used to explore the effects of repeated readings on four dependent variables: reading fluency (words read correctly per minute; wpm), number of errors per minute (epm), types of errors per minute, and answer to literal comprehension questions. Data were collected and analyzed during baseline, intervention, generalization probes, and maintenance probes. Throughout the baseline and intervention phases, participants read a passage aloud and received error correction feedback. During baseline, this was followed by fluency and literal comprehension question assessments. During intervention, this was followed by two oral repeated readings of the passage. Then the fluency and literal comprehension question assessments were administered. Generalization probes followed approximately 25% of all sessions and consisted of a single reading of a new passage at the same readability level. Maintenance sessions occurred 2-, 4-, and 6-weeks after the intervention ended. The results of this study indicated that repeated readings had a positive effect on the reading abilities of ELLs with SLD. Participants read more wpm, made fewer epm, and answered more literal comprehension questions correctly. Additionally, on average, generalization scores were higher in intervention than in baseline. Maintenance scores were varied when compared to the last day of intervention, however, with the exception of the number of hesitations committed per minute maintenance scores were higher than baseline means. This study demonstrated that repeated readings improved the reading abilities of ELLs with SLD and that gains were generalized to untaught passages. Maintenance probes 2-, 4-, and 6- weeks following intervention indicated that mean reading fluency, errors per minute, and correct answers to literal comprehensive questions remained above baseline levels. Future research should investigate the use of repeated readings in ELLs with SLD at various stages of reading acquisition. Further, future investigations may examine how repeated readings can be integrated into classroom instruction and assessments.

The use of a computer graphic organizer for persuasive composition writing by Hispanic students with specific learning disabilities

Many culturally and linguistically diverse (CLD) students with specific learning disabilities (SLD) struggle with the writing process. Particularly, they have difficulties developing and expanding ideas, organizing and elaborating sentences, and revising and editing their compositions (Graham, Harris, & Larsen, 2001; Myles, 2002). Computer graphic organizers offer a possible solution to assist them in their writing. This study investigated the effects of a computer graphic organizer on the persuasive writing compositions of Hispanic middle school students with SLD. A multiple baseline design across subjects was used to examine its effects on six dependent variables: number of arguments and supporting details, number and percentage of transferred arguments and supporting details, planning time, writing fluency, syntactical maturity (measured by T-units, the shortest grammatical sentence without fragments), and overall organization. Data were collected and analyzed throughout baseline and intervention. Participants were taught persuasive writing and the writing process prior to baseline. During baseline, participants were given a prompt and asked to use paper and pencil to plan their compositions. A computer was used for typing and editing. Intervention required participants to use a computer graphic organizer for planning and then a computer for typing and editing. The planning sheets and written composition were printed and analyzed daily along with the time each participant spent on planning. The use of computer graphic organizers had a positive effect on the planning and persuasive writing compositions. Increases were noted in the number of supporting details planned, percentage of supporting details transferred, planning time, writing fluency, syntactical maturity in number of T-units, and overall organization of the composition. Minimal to negligible increases were noted in the mean number of arguments planned and written. Varying effects were noted in the percent of transferred arguments and there was a decrease in the T-unit mean length. This study extends the limited literature on the effects of computer graphic organizers as a prewriting strategy for Hispanic students with SLD. In order to fully gauge the potential of this intervention, future research should investigate the use of different features of computer graphic organizer programs, its effects with other writing genres, and different populations.

The effects of word prediction and text-to-speech technologies on the narrative writing skills of students with specific learning disabilities

This study investigated the effects of word prediction and text-to-speech on the narrative composition writing skills of 6, fifth-grade Hispanic boys with specific learning disabilities (SLD). A multiple baseline design across subjects was used to explore the efficacy of word prediction and text-to-speech alone and in combination on four dependent variables: writing fluency (words per minute), syntax (T-units), spelling accuracy, and overall organization (holistic scoring rubric). Data were collected and analyzed during baseline, assistive technology interventions, and at 2-, 4-, and 6-week maintenance probes. ^ Participants were equally divided into Cohorts A and B, and two separate but related studies were conducted. Throughout all phases of the study, participants wrote narrative compositions for 15-minute sessions. During baseline, participants used word processing only. During the assistive technology intervention condition, Cohort A participants used word prediction followed by word prediction with text-to-speech. Concurrently, Cohort B participants used text-to-speech followed by text-to-speech with word prediction. ^ The results of this study indicate that word prediction alone or in combination with text-to-speech has a positive effect on the narrative writing compositions of students with SLD. Overall, participants in Cohorts A and B wrote more words, more T-units, and spelled more words correctly. A sign test indicated that these perceived effects were not likely due to chance. Additionally, the quality of writing improved as measured by holistic rubric scores. When participants in Cohort B used text-to-speech alone, with the exception of spelling accuracy, inconsequential results were observed on all dependent variables. ^ This study demonstrated that word prediction alone or in combination assists students with SLD to write longer, improved-quality, narrative compositions. These results suggest that word prediction or word prediction with text-to-speech be considered as a writing support to facilitate the production of a first draft of a narrative composition. However, caution should be given to the use of text-to-speech alone as its effectiveness has not been established. Recommendations for future research include investigating the use of these technologies in other phases of the writing process, with other student populations, and with other writing styles. Further, these technologies should be investigated while integrated into classroom composition instruction. ^

Document understanding using data mining and machine learning techniques

With the explosive growth of the volume and complexity of document data (e.g., news, blogs, web pages), it has become a necessity to semantically understand documents and deliver meaningful information to users. Areas dealing with these problems are crossing data mining, information retrieval, and machine learning. For example, document clustering and summarization are two fundamental techniques for understanding document data and have attracted much attention in recent years. Given a collection of documents, document clustering aims to partition them into different groups to provide efficient document browsing and navigation mechanisms. One unrevealed area in document clustering is that how to generate meaningful interpretation for the each document cluster resulted from the clustering process. Document summarization is another effective technique for document understanding, which generates a summary by selecting sentences that deliver the major or topic-relevant information in the original documents. How to improve the automatic summarization performance and apply it to newly emerging problems are two valuable research directions. To assist people to capture the semantics of documents effectively and efficiently, the dissertation focuses on developing effective data mining and machine learning algorithms and systems for (1) integrating document clustering and summarization to obtain meaningful document clusters with summarized interpretation, (2) improving document summarization performance and building document understanding systems to solve real-world applications, and (3) summarizing the differences and evolution of multiple document sources.

Exploring the goals, content, and methods of entrepreneurship professors: A multiple case study

Along with the accumulation of evidence supporting the role of entrepreneurship in economic development (Acs & Armington, 2006; Kuratko, 2005, Reynolds, 2007), governments have persisted in encouraging people to become entrepreneurs (Acs & Stough, 2008; Brannback & Carsrud, 2008). These efforts have tried to reproduce the conditions under which entrepreneurship emerges. One of these conditions is to develop entrepreneurial skills among students and scientists (Fan & Foo, 2004). Entrepreneurship education within higher education has experienced a remarkable expansion in the last 20 years (Green, 2008). To develop entrepreneurial skills among students, scholars have proposed different teaching approaches. However, no clear relationship has been demonstrated between entrepreneurship education, learning outcomes, and business creation (Hostager & Decker, 1999). Despite policy makers demands for more accountability from educational institutions (Klimoski, 2007) and entrepreneurship instructors demands for consistency about what should be taught and how (Maidment, 2009), the appropriate content for entrepreneurship programs remains under constant discussion (Solomon, 2007). Entrepreneurship education is still in its infancy, professors propose diverse teaching goals and radically different teaching methods. This represents an obstacle to development of foundational and consistent curricula across the board (Cone, 2008). Entrepreneurship education is in need of a better conceptualization of the learning outcomes pursued in order to develop consistent curriculum. Many schools do not have enough qualified faculty to meet the growing student demand and a consistent curriculum is needed for faculty development. Entrepreneurship instructors and their teaching practices are of interest because they have a role in producing the entrepreneurs needed to grow the economy. This study was designed to understand instructors’ perspectives and actions related to their teaching. The sample studied consisted of eight college and university entrepreneurship instructors. Cases met predetermined criteria of importance followed maximum variation strategies. Results suggest that teaching content were consistent across participants while different teaching goals were identified: some instructors inspire and develop general skills of students while others envision the creation of a real business as the major outcome of their course. A relationship between methods reported by instructors and their disciplinary background, teaching perspective, and entrepreneurial experience was found.

The use of computer graphic organizers for narrative writing by elementary school students with Specific Learning Disabilities

Writing is an academic skill critical to students in today's schools as it serves as a predominant means for demonstrating knowledge during school years (Graham, 2008). However, for many students with Specific Learning Disabilities (SLD), learning to write is a challenging, complex process (Lane, Graham, Harris, & Weisenbach, 2006). Students SLD have substantial writing challenges related to the nature of their disability (Mayes & Calhoun, 2005). ^ This study investigated the effects of computer graphic organizer software on the narrative writing compositions of four, fourth- and fifth-grade, elementary-level boys with SLD. A multiple baseline design across subjects was used to explore the effects of the computer graphic organizer software on four dependent variables: total number of words, total planning time, number of common story elements, and overall organization. ^ Prior to baseline, participants were taught the fundamentals of narrative writing. Throughout baseline and intervention, participants were read a narrative writing prompt and were allowed up to 10 minutes to plan their writing, followed by 15 minutes for writing, and 5 minutes of editing. During baseline, all planning was done using paper and pencil. During intervention, planning was done on the computer using a graphic organizer developed from the software program Kidspiration 3.0 (2011). All compositions were written and editing was done using paper and pencil during baseline and intervention. ^ The results of this study indicated that to varying degrees computer graphic organizers had a positive effect on the narrative writing abilities of elementary aged students with SLD. Participants wrote more words (from 54.74 to 96.60 more), planned for longer periods of time (from 4.50 to 9.50 more minutes), and included more story elements in their compositions (from 2.00 to 5.10 more out of a possible 6). There were nominal to no improvements in overall organization across the 4 participants. ^ The results suggest that teachers of students with SLD should considering use computer graphic organizers in their narrative writing instruction, perhaps in conjunction with remedial writing strategies. Future investigations can include other types of writing genres, other stages of writing, participants with varied demographics and their use combined with remedial writing instruction. ^