An empirical study on behavioural intention to reuse e-learning systems in rural China

The learner’s acceptance of e-learning systems has received extensive attention in prior studies, but how their experience of using e-learning systems impacts on their behavioural intention to reuse those systems has attracted limited research. As the applications of e-learning are still gaining momentum in developing countries, such as China, it is necessary to examine the relationships between e-learners’ experience and perceptions and their behavioural intention to reuse, because it is argued that system reuse is an important indicator of the system’s success. Therefore, a better understanding of the multiple factors affecting the e-learner’s intention to reuse could help e-learning system researchers and providers to develop more effective and acceptable e-learning systems. Underpinned by the information system success model, technology acceptance model and self-efficacy theory, a theoretical framework was developed to investigate the learner’s behavioural intention to reuse e-learning systems. A total of 280 e-learners were surveyed to validate the measurements and proposed research model. The results demonstrated that e-learning service quality, course quality, perceived usefulness, perceived ease of use and self-efficacy had direct effects on users’ behavioural intention to reuse. System functionality and system response have an indirect effect, but system interactivity had no significant effect. Furthermore, self-efficacy affected perceived ease of use that positively influenced perceived usefulness

Fidelity Assessment in Community Programs: An Approach to Validating Simplified Methodology.

Fidelity to intervention protocol is linked to best outcomes for individuals with autism spectrum disorder (ASD; see Boyd & Corley [Autism 5(4):430-441, 2001]; Pellecchia et al. [J Autism Dev Disord 45(9):2917-2927, 2015]); however, fidelity measurement tools that are both accurate and feasible for community use are often not available. In this paper we explore methods for validated simplification of fidelity assessment procedures toward the goal of increased use in clinical practice. Video recordings (n = 36) of therapists working with children with ASD were coded using three variations of fidelity assessment methodology (trial-by-trial, 5-point Likert Scale, and 3-point Likert Scale), and the results were compared for exact agreement, mastery criterion agreement, and overall reliability. The results indicated overall a very high percentage of exact agreement (mean 99.44%, range 94.4-100%) and excellent reliability (mean Krippendorff's alpha [Kα] 1.0) between the trial-by-trial and 5-point Likert Scale across all components; however, the 3-point method may be viewed as being the more feasible strategy within community programs

Validating simulated interaction for retrieval evaluation

A searcher’s interaction with a retrieval system consists of actions such as query formulation, search result list interaction and document interaction. The simulation of searcher interaction has recently gained momentum in the analysis and evaluation of interactive information retrieval (IIR). However, a key issue that has not yet been adequately addressed is the validity of such IIR simulations and whether they reliably predict the performance obtained by a searcher across the session. The aim of this paper is to determine the validity of the common interaction model (CIM) typically used for simulating multi-query sessions. We focus on search result interactions, i.e., inspecting snippets, examining documents and deciding when to stop examining the results of a single query, or when to stop the whole session. To this end, we run a series of simulations grounded by real world behavioral data to show how accurate and responsive the model is to various experimental conditions under which the data were produced. We then validate on a second real world data set derived under similar experimental conditions. We seek to predict cumulated gain across the session. We find that the interaction model with a query-level stopping strategy based on consecutive non-relevant snippets leads to the highest prediction accuracy, and lowest deviation from ground truth, around 9 to 15% depending on the experimental conditions. To our knowledge, the present study is the first validation effort of the CIM that shows that the model’s acceptance and use is justified within IIR evaluations. We also identify and discuss ways to further improve the CIM and its behavioral parameters for more accurate simulations

Designing and Piloting a Tool for the Measurement of the Use of Pronunciation Learning Strategies

What appears to be indispensable to drive the field forward and ensure that research findings will be comparable across studies and provide a sound basis for feasible pedagogic proposals is to draw up a classification of PLS and design on that basis a valid and reliable data collection tool which could be employed to measure the use of these strategies in different groups of learners, correlate it with individual and contextual variables, and appraise the effects of training programs. In accordance with this rationale, the present paper represents an attempt to propose a tentative categorization of pronunciation learning strategies, adopting as a point of reference the existing taxonomies of strategic devices (i.e. O'Malley and Chamot 1990; Oxford 1990) and the instructional options teachers have at their disposal when dealing with elements of this language subsystem (e.g. Kelly 2000; Goodwin 2001). It also introduces a research instrument designed on the basis of the classification that shares a number of characteristics with Oxford's (1990) Strategy Inventory for Language Learning but, in contrast to it, includes both Likert-scale and open-ended items. The findings of a pilot study which involved 80 English Department students demonstrate that although the tool requires considerable refinement, it provides a useful point of departure for future research into PLS

Recognizing Objects In-the-wild: Where Do We Stand?

The ability to recognize objects is an essential skill for a robotic system acting in human-populated environments. Despite decades of effort from the robotic and vision research communities, robots are still missing good visual perceptual systems, preventing the use of autonomous agents for real-world applications. The progress is slowed down by the lack of a testbed able to accurately represent the world perceived by the robot in-the-wild. In order to fill this gap, we introduce a large-scale, multi-view object dataset collected with an RGB-D camera mounted on a mobile robot. The dataset embeds the challenges faced by a robot in a real-life application and provides a useful tool for validating object recognition algorithms. Besides describing the characteristics of the dataset, the paper evaluates the performance of a collection of well-established deep convolutional networks on the new dataset and analyzes the transferability of deep representations from Web images to robotic data. Despite the promising results obtained with such representations, the experiments demonstrate that object classification with real-life robotic data is far from being solved. Finally, we provide a comparative study to analyze and highlight the open challenges in robot vision, explaining the discrepancies in the performance

Italian translation of the questionnaire for professional training evaluation

This works illustrates the psychometric properties of the Italian version of the Questionnaire for Professional Training Evaluation (Q4TE), validated by Grohmann and Kauffeld (2013). This 12-item questionnaire provides evaluation for different training outcomes, it is time efficient and applicable to several professional contexts, and it shows sound psychometric properties. In order to test the Italian form, we led two studies. In study 1 (N=125), an EFA led to a two-factor solution accounting for short and long-term training outcomes. In study 2 (N=122) a five-model comparison was performed. Although at a first stage a two factor solutions seemed to emerge, CFA found the best fit in a 6 inter-correlated first-order factors model (satisfaction, utility, knowledge, application to practice, individual organizational results and global organizational results). Relationships with learning transfer, transfer quantity, type of training, training methodologies, and individual variables (gender, age, tenure) are explored. Limitations, research and practical implications are discussed

Comparing alignment factors in SMEs and large organizations: a planning integration perspective

Measurement of the alignment between business strategies and information systems (IS) has demonstrated positive impact for the organizational performance. The factors that have proved relevant when assessing the maturity level of alignment are: communication, competency/value measurement, governance, partnership, architecture & scope, and skills. Existing research, however, has focused on the assessment of these factors on large organizations and has barely explored their impact on Small and Medium Enterprises (SME). This paper uses the data provided by 127 participants from large and SMEs in order to identify whether the aforementioned factors are also relevant for assessing the level of alignment maturity in SMEs. The results from this research suggest that there are not significant differences between large organizations and SMEs when assessing those factors. In addition, this research also explored the relation between different planning integration of alignment (independent, sequential and simultaneous) in order to measure the perceived relevance of the factors. The results suggest that the planning integration identified on SMEs and large organizations has a positive correlation on how these factors are ranked. For both SMEs and large organizations where the formulation is simultaneous, the relevance of the factors is higher perceived than it is for those where the formulation is independent or sequential