Educators\u27 Perceptions of Twitter for Educational Technology Professional Development: A Uses and Gratifications Expectancy Model

Throughout the years, the practice of professional development amongst educators has evolved to adapt to the needs of a changing society and a shift to online professional development (OPD) opportunities has become popular for meeting the needs of educators. As a result, social media platforms, like Twitter, have grown in popularity as outlets for OPD; however, little research has been conducted to evaluate why educators are seeking professional development opportunities through social media platforms. This exploratory study proposed to examine how educators\u27 uses and gratifications expectancy of Twitter for professional development influences their perceived e-learning experience. In addition, it sought to investigate the demographics of participants who were seeking educational technology knowledge through Twitter. Based on a review of literature, a uses and gratifications approach was the proposed theoretical model for evaluating how and why educators\u27 perceived e-learning experience was affected by four uses and gratification expectancy constructs. The participants included any educators who utilized the #edtechchat hashtags on Twitter, which is devoted to the sharing of educational technology knowledge, as well as weekly, organized Twitter chats on topics related to educational technology. The data was collected through a Web-based survey based on an adapted version of Mondi, Woods, and Rafi (2008) Uses and Gratification Expectancy Questionnaire, where the researchers examined how and why students\u27 uses and gratification expectancy (UGE) for e-learning resources influenced their perceived e-learning experience. The data was analyzed through Pearson correlation coefficient and a stepwise multiple regression to discover which UGE constructs predicted educators\u27 perceived e-learning experience. All four UGE constructs showed significant effects on perceived e-learning experience; however, the stepwise regression results showed cognitive uses and gratifications expectancy to be the only significant predictor of perceived e-learning experience. The findings of this research supports previous research into uses and gratifications of Internet-based tools and may help Twitter chat moderators plan their efforts for coordinating effective professional development experiences

Evaluating the success of e-learning systems : the case of Moodle LMS at the University of Warwick

E-learning is a direct result of the integration of education and technology, and is increasingly considered as a powerful medium for learning. The undeniable significance of e-learning in education has led to a large growth of e-learning courses and systems offering different types of service. Thus, evaluation of e-learning systems is vital in ensuring successful delivery, effective use, and positive impact on learners. In recent studies, the vast majority of universities report having adopted varieties of e-learning systems and platforms to facilitate the students’ learning process. However, while adopting e-learning systems is useful, it is not an end in itself. In reviewing the literature, studies have revealed many problems with these systems, such as meeting users’ requirements and the suitability of these systems for targeted users. In order to improve the current systems to satisfy users’ needs, it is important to understand the different aspects that influence the quality and success of these systems. Hence, a new model for evaluating the success of e-learning systems is introduced in this research. Based on an intensive review of the literature, four approaches were identified and analysed as a theoretical basis for the research: DeLone and McLean’s information systems success model; the Technology Acceptance Model; the User Satisfaction Models; and the E-learning Quality Models. In order to provide a general comprehensive definition of e-learning success measurements, the four approaches found in the literature were considered in developing our model. The proposed model includes eleven constructs: technical system quality; information quality; service quality; educational system quality; support system quality; learner quality; instructor quality; perceived satisfaction; perceived usefulness; system use; and benefits. The model is comprehensive, and not based on the number of constructs, but on the intention to provide a holistic picture and different levels of success related to a broad range of success determinants, rather than focusing on a specific construct. As such, it forms an original contribution to knowledge. To test the model, an empirical study was conducted. First, an instrument was designed to assess the perceptions of students towards e-learning system success. Second, an expert study with 30 e-learning experts was carried out to confirm the measurements and indicators. The model was then tested in the context of the University of Warwick by fitting the model to data collected from 563 students engaged with an e-learning system. Both quantitative and qualitative data were analysed. The results confirm that the model proposed in this study is valid and reliable. Thus, the study contributes to the growing body of knowledge with a valid and reliable model and an instrument to evaluate e-learning systems success (EESS model). Further, the study sheds light on important issues and recommendations that should be taken into consideration to improve the perceptions of satisfaction, usefulness, use, and benefits of the e-learning systems. The study further provides practitioners with several practical contributions

Putting theory oriented evaluation into practice

Evaluations of gaming simulations and business games as teaching devices are typically end-state driven. This emphasis fails to detect how the simulation being evaluated does or does not bring about its desired consequences. This paper advances the use of a logic model approach which possesses a holistic perspective that aims at including all elements associated with the situation created by a game. The use of the logic model approach is illustrated as applied to Simgame, a board game created for secondary school level business education in six European Union countries

Evaluation methods and decision theory for classification of streaming data with temporal dependence

Predictive modeling on data streams plays an important role in modern data analysis, where data arrives continuously and needs to be mined in real time. In the stream setting the data distribution is often evolving over time, and models that update themselves during operation are becoming the state-of-the-art. This paper formalizes a learning and evaluation scheme of such predictive models. We theoretically analyze evaluation of classifiers on streaming data with temporal dependence. Our findings suggest that the commonly accepted data stream classification measures, such as classification accuracy and Kappa statistic, fail to diagnose cases of poor performance when temporal dependence is present, therefore they should not be used as sole performance indicators. Moreover, classification accuracy can be misleading if used as a proxy for evaluating change detectors with datasets that have temporal dependence. We formulate the decision theory for streaming data classification with temporal dependence and develop a new evaluation methodology for data stream classification that takes temporal dependence into account. We propose a combined measure for classification performance, that takes into account temporal dependence, and we recommend using it as the main performance measure in classification of streaming data

Soft behaviour modelling of user communities

A soft modelling approach for describing behaviour in on-line user communities is introduced in this work. Behaviour models of individual users in dynamic virtual environments have been described in the literature in terms of timed transition automata; they have various drawbacks. Soft multi/agent behaviour automata are defined and proposed to describe multiple user behaviours and to recognise larger classes of user group histories, such as group histories which contain unexpected behaviours. The notion of deviation from the user community model allows defining a soft parsing process which assesses and evaluates the dynamic behaviour of a group of users interacting in virtual environments, such as e-learning and e-business platforms. The soft automaton model can describe virtually infinite sequences of actions due to multiple users and subject to temporal constraints. Soft measures assess a form of distance of observed behaviours by evaluating the amount of temporal deviation, additional or omitted actions contained in an observed history as well as actions performed by unexpected users. The proposed model allows the soft recognition of user group histories also when the observed actions only partially meet the given behaviour model constraints. This approach is more realistic for real-time user community support systems, concerning standard boolean model recognition, when more than one user model is potentially available, and the extent of deviation from community behaviour models can be used as a guide to generate the system support by anticipation, projection and other known techniques. Experiments based on logs from an e-learning platform and plan compilation of the soft multi-agent behaviour automaton show the expressiveness of the proposed model

Multimodal Hierarchical Dirichlet Process-based Active Perception

In this paper, we propose an active perception method for recognizing object categories based on the multimodal hierarchical Dirichlet process (MHDP). The MHDP enables a robot to form object categories using multimodal information, e.g., visual, auditory, and haptic information, which can be observed by performing actions on an object. However, performing many actions on a target object requires a long time. In a real-time scenario, i.e., when the time is limited, the robot has to determine the set of actions that is most effective for recognizing a target object. We propose an MHDP-based active perception method that uses the information gain (IG) maximization criterion and lazy greedy algorithm. We show that the IG maximization criterion is optimal in the sense that the criterion is equivalent to a minimization of the expected Kullback--Leibler divergence between a final recognition state and the recognition state after the next set of actions. However, a straightforward calculation of IG is practically impossible. Therefore, we derive an efficient Monte Carlo approximation method for IG by making use of a property of the MHDP. We also show that the IG has submodular and non-decreasing properties as a set function because of the structure of the graphical model of the MHDP. Therefore, the IG maximization problem is reduced to a submodular maximization problem. This means that greedy and lazy greedy algorithms are effective and have a theoretical justification for their performance. We conducted an experiment using an upper-torso humanoid robot and a second one using synthetic data. The experimental results show that the method enables the robot to select a set of actions that allow it to recognize target objects quickly and accurately. The results support our theoretical outcomes.Comment: submitte