An interaction analysis model to study knowledge construction in xMOOCs

 One of the recent tools of online teaching and learning has been MOOC which utilizes the web and can be considered a critical factor in delivering future lessons. Coursera is an approved and famous online learning tool established by two professors from Stanford. According to previous studies, Coursera concentrates on duplicating knowledge instead of constructing it. The present work aims at observing Coursera community in its normal context and investigating how participants construct knowledge. The required data were gathered through archive data including the top and chosen posts of online discussion groups. The interaction analysis model (IAM) was used to qualitatively analyze the data. In the observed courses, it was found that Coursera is mostly at Phase I (sharing/comparing information) of the IAM. Using the IAM which was first proposed for investigations of online debates, the research found a new phase and operations for assessing the level of knowledge construction in online discussion forums. This research bridges the gaps in the related literature by providing a foundation for understanding knowledge construction in the xMOOC context. Besides, this research developed understanding for future work which is a Coursera community framework that generally makes a MOOC community more potential to construct knowledge

Determinant factors of cyberbullying: an application of theory of planned behavior

Employing the Theory of Planned Behavior (TPB), this study aims to find determinant factors that have influence over individuals’ intention to cyberbully others. Along with the TPB’s main variables, including attitude, subjective norms and perceived behavioral control, in this research, the role of moral obligation, perceived threat of legal punishment and overall gain was also studied. Using a scenario-based questionnaire, the data were collected from 96 students in Universiti Teknologi Malaysia. According to the results, subjective norms and overall gain reflected to be significant over cyberbullying intention, while the rest of variables did not reflect any significance. The impact of these determinants was not the same among females and males, which reflect gender differences matters in intention to cyberbully

Determinant factors of cyberbullying: an application of theory of planned behavior

Employing the Theory of Planned Behavior (TPB), this study aims to find determinant factors that have influence over individuals’ intention to cyberbully others. Along with the TPB’s main variables, including attitude, subjective norms and perceived behavioral control, in this research, the role of moral obligation, perceived threat of legal punishment and overall gain was also studied. Using a scenario-based questionnaire, the data were collected from 96 students in Universiti Teknologi Malaysia. According to the results, subjective norms and overall gain reflected to be significant over cyberbullying intention, while the rest of variables did not reflect any significance. The impact of these determinants was not the same among females and males, which reflect gender differences matters in intention to cyberbully

Knowledge discovery for large databases in education institutes

This project presents the patterns and relations between attributes of Iran Higher Education (Iran Higher Education) data gained from the use of data mining techniques to discover knowledge and use them in decision making system of IHE. Large dataset of IHE is difficult to analysis and display, since they are significant for decision making in IHE. This study utilized the famous data mining software, Weka and SOM to mine and visualize IHE data. In order to discover worthwhile patterns we used clustering techniques and visualized the results. The selected dataset includes data of five medical university of Tehran as a small data set and Ministry of Science - Research and Technology’s universities as a larger data set. Knowledge discovery and visualization are necessary for analyzing of these datasets. Our analysis reveals some knowledge in higher education aspect related to program of study, degree in each program, learning style, study mode and other IHE attributes. This study helps to IHE to discover knowledge in a visualize way; our results can be focused more by experts in higher education field to assess and evaluate more

A naive recommendation model for large databases

It is difficult for users to find items as the number of choices increase and they become overwhelmed with high volume of data. In order to avoid them from bewilderment, a recommender could be applied to find more related items in shorter time. In this paper, we proposed a naive recommender model which uses Association Rules Mining technique to generate two item sets enabling to find all existing rules for a certain item and has the capability to search on demand which decrease the response time dramatically This model mines transactions’ database to discover the existing rules among items and stores them in a sparse matrix. It also searches the matrix by means of a naive algorithm to generate a search list.We have applied and evaluated our model in Universiti Teknologi Malaysia and the results reflect a high level of accuracy

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Cloud computing for teaching practice: a new design?

Recently researchers have shown an increased interest in cloud computing technology. It is becoming increasingly difficult to ignore cloud computing technology in education context. However rapid changes in information technology are having a serious effect on teaching framework designs. So far, however, there has been little discussion about cloud computing benefits in domains of teaching frameworks which propels us to study and redesign teaching frameworks considering cloud computing. The purpose of this paper is to review recent research into cloud computing and features which can be improved with this new technology. This paper studied several researches through literature to determine the main impact of cloud computing on "planning and preparation" and "instruction" domains as two main domains of teaching framework. Light will be then shed on the impact and potential benefits of cloud computing on teaching framework. The paper closes by proposing to design an evaluation table based on cloud computing artifacts to enhance teaching practice and highlights its offerings for educational institutions

Application of self organizing map for knowledge discovery based in higher education data

This paper focuses on knowledge discovery among attributes of Iran Higher Education Institute using self organizing map (SOM); the key problem with massive volume of data is extracting knowledge and patterns that are hidden in data. Managerial needs to explore this data for the purpose of decision making and strategy making reveals its importance. Furthermore it can be useful for researchers that study and research about higher education. Meanwhile planning for higher education has significant impact on developing of one society, successful planning needs to analysis some huge and historical data that is available in higher education institutes. SOM is a particular type of neural network used in clustering and helps discover patterns and relations without advanced knowledge about them. The steps of this approach can be discussed under five headings, which are (i) Data Preparation (ii) Data Loading, (iii) Initializing, (iv) Map training and (v) Interpretation of the results. The target dataset contains data of five universities located in Tehran, Iran affiliated to Medical Ministry of Iran and the most important attributes are program of study, learning style, study mode and degree. Results show that the number of enrolling students for Tehran medical university has decreased for the past 23 years from 1988 to 2005. This study also finds that Tehran University of Medical Science covers the majority of high degrees like MDdisplay(Doctor of Medicine) and PhD. The findings of this study can be used in improving of higher education decision making systems and the results of this study indicate SOM toolbox utility in similar institutes to knowledge discovery in a visualizing way

The Coursera Community Framework: exploring the MOOC as a Community of Practice

Massive open online courses (MOOCs) have increasingly become an important element for individuals’ learning and development. However, MOOCs mainly concentrate on duplicating knowledge instead of constructing it. This research aims to explore the structure of the MOOCs for fostering the knowledge construction in which educational professional build, develop, share one another’ learning and reflections. This research focused on Coursera, a particular MOOC community, by drawing on the concepts of community of practice (CoP) as a theoretical lens. Three types of data were collected. The archival data consisted of the top and selected posts from online discussion forums, and the elicited data which was derived from over 60 interviews with Coursera learners. Meanwhile, field note data was extracted from 160 days of interaction with the participants. A qualitative research method using a netnographic methodology was employed. The findings contribute to the body of knowledge construction and online communities by providing an understanding of the domain, community and practice elements. The study on other elements such as the reinforcement of identity, formation of warrants and identification of mechanisms for legitimate peripheral participation can help to interpret the constitution of CoPs in MOOC. This research developed a Coursera community framework that generally makes a MOOC community more energetic to construct knowledge