In the era of internet, universities and higher education institutions are increasingly tend to provide e-learning. For suitable planning and more enjoying the benefits of this educational approach, a model for measuring success of e-learning systems is essential. So in this paper, we try to survey and present a model for measuring success of e-learning systems in universities. For this purpose, at first, according to literature review, a conceptual model was designed. Then, based on opinions of 33 experts, and assessing their suggestions, research indicators were finalized. After that, to examine the relationships between components and finalize the proposed model, a case study was done in 5 universities: Amir Kabir University, Tehran University, Shahid Beheshti University, Iran University of Science & Technology and Khaje Nasir Toosi University of Technology. Finally, by analyzing questionnaires completed by 369 instructors, students and alumni, which were e-learning systems user, the final model (MELSS Model).

A model for measuring e-learning systems success in universities

In this paper, we survey the methods and concepts developed for the evaluation of dialogue systems. Evaluation, in and of itself, is a crucial part during the development process. Often, dialogue systems are evaluated by means of human evaluations and questionnaires. However, this tends to be very cost- and time-intensive. Thus, much work has been put into finding methods which allow a reduction in involvement of human labour. In this survey, we present the main concepts and methods. For this, we differentiate between the various classes of dialogue systems (task-oriented, conversational, and question-answering dialogue systems). We cover each class by introducing the main technologies developed for the dialogue systems and then present the evaluation methods regarding that class.

/pdf/survey-on-evaluation-methods-for-dialogue-systems-2jg1pygand.pdf

Survey on evaluation methods for dialogue systems

http://www.univ-soukahras.dz/eprints/2016-1-6017f.pdf

Exploring user satisfaction for e-learning systems via usage-based metrics and system usability scale analysis

In this paper we survey the methods and concepts developed for the evaluation of dialogue systems. Evaluation is a crucial part during the development process. Often, dialogue systems are evaluated by means of human evaluations and questionnaires. However, this tends to be very cost and time intensive. Thus, much work has been put into finding methods, which allow to reduce the involvement of human labour. In this survey, we present the main concepts and methods. For this, we differentiate between the various classes of dialogue systems (task-oriented dialogue systems, conversational dialogue systems, and question-answering dialogue systems). We cover each class by introducing the main technologies developed for the dialogue systems and then by presenting the evaluation methods regarding this class.

/pdf/survey-on-evaluation-methods-for-dialogue-systems-hso8hcx0wy.pdf

Survey on Evaluation Methods for Dialogue Systems

Welcome to the CHI 2006 Extended Abstracts. We hope that you will enjoy this year's extended abstracts and the changes that we have made. For the first time this year, we encouraged submissions from six different sub-communities of the human-computer interaction (HCI) field: design; education; engineering; management; research and usability. The intent was to make sure that the technical program included elements that would be of interest to the broad HCI world. We especially sought Experience Reports, Panels, SIGs, and HCI Overviews. Submissions to each of these were carefully reviewed by the Conference Committee and their respective Community Chairs. These Extended Abstracts include these new or modified venues as well as materials from other traditional CHI venues.

CHI '06 Extended Abstracts on Human Factors in Computing Systems

In this paper we describe an approach to efficiently evaluate the usability of an interactive application that has been realized to support various platforms and modalities. Therefore we combine our Multi-Access Service Platform (MASP), a model-based runtime environment to offer multimodal user interfaces with the MeMo workbench which is a tool supporting an automated usability analysis. Instead of deriving a system model by reverse-engineering or annotating screenshots for the automated usability analysis, we use the semantics of the runtime models of the MASP. This allows us to reduce the evaluation effort by automating parts of the testing process for various combinations of platforms and user groups that should be addressed by the application. Furthermore, by testing the application at runtime, the usability evaluation can also consider system dynamics and information that are unavailable at design time.

/pdf/automated-usability-evaluation-during-model-based-4kopz4a4zf.pdf

Automated Usability Evaluation during Model-Based Interactive System Development

Memo workbench for semi-automated usability testing.

Adaptive user interfaces are commonly used for providing different layouts and information according to the current context-of-use. However, the complexity and heterogeneity of potential users, platforms and environments lead to a combinatorial explosion of variants, making it almost impossible to foresee all potential results of adaptations at design time. In this paper we present our work on the automatic evaluation of usability aspects of adaptive user interfaces at runtime which is supposed to be used complementary to design time usability evaluation. We show how a user interface model, providing different adaptation alternatives, can be combined with a model of the current user to simulate interaction and evaluate the feasibility of different adaptations.

/pdf/evaluating-user-interface-adaptations-at-runtime-by-4ap6p6o4vw.pdf

Evaluating user interface adaptations at runtime by simulating user interaction

Adaptive applications have the potential to help users with special and specific needs. However, evaluating the usability of such adaptive applications tends to become very complex. This chapter presents an integrated concept for the automated usability evaluation of model-based adaptive user interfaces. The approach is supposed to be used complementary to custom usability evaluations at an early stage of development. Interaction of a user is simulated and evaluated by combining a user model with user interface models from a model-based development framework, which is capable of providing different adaptation alternatives based on user attributes and the context of use. The main benefit of the approach is that no additional descriptions of the application’s UI and tasks need to be created for the usability evaluation because they are already available from the development process. As a result, different design alternatives and adaptation variants can be compared under equal usability evaluation criteria. Further, the complexity and costs for applying automated usability evaluation to adaptive user interfaces for users with special and specific needs can be reduced.

Automated Usability Evaluation of Model-Based Adaptive User Interfaces for Users with Special and Specific Needs by Simulating User Interaction

Adaptive user interfaces (UI) offer the opportunity to adapt to changes in the context, but this also poses the challenge of evaluating the usability of many different versions of the resulting UI. Consequently, usability evaluations tend to become very complex and time-consuming. We describe an approach that combines model-based usability evaluation with development models of adaptive UIs. In particular, we present how a cognitive user behavior model can be created automatically from UI development models and thus save time and costs when predicting task execution times. With the help of two usability studies, we show that the resulting predictions can be further improved by using information encoded in the UI development models.

/pdf/predicting-task-execution-times-by-deriving-enhanced-24dr6avog4.pdf

Predicting task execution times by deriving enhanced cognitive models from user interface development models

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