Applying a Modified Technology Acceptance Model to Qualitatively Analyse the Factors Affecting Microblogging Integration

The purpose of this research is to examine factors affecting students’ perception and engagement of microblogging integration using a qualitative approach. We employed a qualitative case study design to explore potential factors affecting microblogging integration in a hybrid course. Using the technology acceptance model (TAM) model as an umbrella framework, we examined through in-depth interviews with 18 participants the impact of microblogging integration into instruction that affected students’ reported use and perceptions of their microblogging-supported learning experiences. We found that individual differences, system characteristics, social influence and facilitating conditions all have impact on student participation and engagement in microblogging integration to varying degrees. We identified more granular factors within each of the four dimensions. Additionally, we proposed a Twitter user taxonomy based on perceived usefulness and usage behaviour and discussed its broad implications in higher education learning environments. Finally, we identified several pedagogical implications pertaining to strategies of microblogging integration under the context of a hybrid course and offered pertinent recommendations for future research

Benefits of using data mining techniques to extract and analyze Twitter data for higher education applications: a systematic literature review

In recent years, there has been a growing interest by education actors to include TIC in their institutions; as well as social networks, far from being a problem and their use aimed, permit innovate traditional classes and improve communication between teachers and students This study has two objectives: (1) conduct a systematic literature review through searching papers published between January/2007 and March/2019 in data bases like as ACM, IEEE, ScienceDirect, Springer and others, to evidence researches that apply data mining techniques to extract and analyze Twitters data in higher education; and (2) to emphasize pedagogic practices that include Twitter and data mining to improve education process. From 315 papers obtained, only 65 fulfilled inclusion criteria. The main results indicate that: (1) the most used data mining techniques are predictive with classification tasks; (2) Twitter is principally used to: (a) determinate perception; (b) share information, materials and resources; (c) generate communication and participation; (d) promote abilities and (e) improve oral expression and academic performance; (3) United States has the most numbers of researches in this area; however, in Latin-American countries findings are not enough, so, there a new area to investigate in this region and (4) researches used models, methods, strategies, theories and instruments as a pedagogic practice; so that, there wasn’t an agreement about a shape to include Twitter data extracting in higher education to improve teaching and learning process.En los últimos años, existe un creciente interés por los actores de la educación en la inclusión de las TIC en sus instituciones, como es el caso de las redes sociales, que lejos de ser un problema y mediante un uso guiado de las mismas, permiten innovar las sesiones de clases tradicionales y mejorar la comunicación entre docentes y estudiantes. En el presente estudio se plantearon dos objetivos: (1) realizar una revisión sistemática de la literatura, mediante la búsqueda de artículos publicados entre Enero/2007 y Marzo/2019, en bases de datos como ACM, IEEE, ScienceDirect, Springer, entre otras, para identificar las investigaciones que han aplicado técnicas de minería de datos, para la extracción y análisis de datos de Twitter en la educación superior; y, (2) destacar las prácticas pedagógicas que han incorporado Twitter y minería de datos para mejorar los procesos educativos. De los 315 artículos obtenidos, fueron seleccionados 65 que cumplieron con los criterios de inclusión. Los principales resultados indican que: (1) las técnicas de minería de datos más utilizadas son predictivas con tareas de clasificación; (2) Twitter se usa principalmente para: (a) determinar percepción estudiantil; (b) compartir información, material y recursos; (c) generar comunicación y participación; (d) fomentar habilidades; y (e) mejorar la expresión oral y el rendimiento académico; (3) Estados Unidos es el país con mayor número de trabajos; sin embargo, en países de Latinoamérica los hallazgos son pocos, por lo que, se apertura un campo de investigación en esta región; y (4) los estudios incluyeron modelos, métodos, estrategias, teorías o instrumentos como práctica pedagógica; de modo que, no existe un consenso en la forma en que los datos extraídos de Twitter podrían ser incorporados en la educación superior para mejorar los procesos de enseñanza y aprendizaje

Benefits of using data mining techniques to extract and analyze Twitter data for higher education applications: a systematic literature review

En los últimos años, existe un creciente interés por los actores de la educación en la inclusión de las TIC en sus instituciones, como es el caso de las redes sociales, que lejos de ser un problema y mediante un uso guiado de las mismas, permiten innovar las sesiones de clases tradicionales y mejorar la comunicación entre docentes y estudiantes. En el presente estudio se plantearon dos objetivos: (1) realizar una revisión sistemática de la literatura, mediante la búsqueda de artículos publicados entre enero/2007 y marzo/2019, en bases de datos como ACM, IEEE, ScienceDirect, Springer, entre otras, para identificar las investigaciones que han aplicado técnicas de minería de datos, para la extracción y análisis de datos de Twitter en la educación superior; y, (2) destacar las prácticas pedagógicas que han incorporado Twitter y minería de datos para mejorar los procesos educativos. De los 315 artículos obtenidos, fueron seleccionados 65 que cumplieron con los criterios de inclusión. Los principales resultados indican que: (1) las técnicas de minería de datos más utilizadas son predictivas con tareas de clasificación; (2) Twitter se usa principalmente para: (a) determinar percepción estudiantil; (b) compartir información, material y recursos; (c) generar comunicación y participación; (d) fomentar habilidades; y (e) mejorar la expresión oral y el rendimiento académico; (3) Estados Unidos es el país con mayor número de trabajos; sin embargo, en países de Latinoamérica los hallazgos son pocos, por lo que se apertura un campo de investigación en esta región; y (4) los estudios incluyeron modelos, métodos, estrategias, teorías o instrumentos como práctica pedagógica; de modo que no existe un consenso en la forma en que los datos extraídos de Twitter podrían ser incorporados en la educación superior para mejorar los procesos de enseñanza y aprendizaje.In recent years, there has been a growing interest by education actors to include TIC in their institutions; as well as social networks, far from being a problem and their use aimed, permit innovate traditional classes and improve communication between teachers and students This study has two objectives: (1) conduct a systematic literature review through searching papers published between January/2007 and March/2019 in data bases like as ACM, IEEE, ScienceDirect, Springer and others, to evidence researches that apply data mining techniques to extract and analyze Twitters data in higher education; and (2) to emphasize pedagogic practices that include Twitter and data mining to improve education process. From 315 papers obtained, only 65 fulfilled inclusion criteria. The main results indicate that: (1) the most used data mining techniques are predictive with classification tasks; (2) Twitter is principally used to: (a) determinate perception; (b) share information, materials and resources; (c) generate communication and participation; (d) promote abilities and (e) improve oral expression and academic performance; (3) United States has the most numbers of researches in this area; however, in Latin-American countries findings are not enough, so, there a new area to investigate in this region and (4) researches used models, methods, strategies, theories and instruments as a pedagogic practice; so that, there wasn’t an agreement about a shape to include Twitter data extracting in higher education to improve teaching and learning process.Instituto de Investigación en Informátic

A micro note taking approach : the student experience

Note taking is one of the most widely-practised and commonly used activities among students in the classroom. However, despite the massive advancement of technology in education, pen and paper still seem to be the (most) favoured note taking approach among students. This, however, could be the result of not having note taking technology that provides students with relative advantages and substantial value in comparison to pen and paper. On the other hand, social media has been growing in popularity. Short messages can be easily conveyed via microblogging applications, such as Twitter. Therefore, the research aims to investigate the effect of using the short content creation feature of microblogging (140 characters) as a note capturing approach in the classroom. This research adopted a design science research methodology consisting of three phases. The first phase, investigation, reviewed the literature and conducted an exploratory study. The literature review showed that there is an increased interest in using technology for learning activities. However, the existent technological support for note taking, in particular, is not popular. In addition, an exploratory study was conducted with 254 undergraduate students at the University of Warwick. The study showed that students had a lack of interest in taking notes using current note taking application on their mobile devices. Hence, to tackle this issue, the development phase proposed a micro note taking mobile application to support students’ note taking at university-level. In addition, this phase included the development and implementation of the mobile micro note taking application (M2NT) based on microblogging technology for data collection purposes. Finally, the evaluation phase included a main experiment conducted with 42 students using three types of note taking approach (i.e. pen and paper, word processor, and the micro note taking application). This was followed up with questionnaires distributed to students after experiencing each note taking approach. In addition, the experiment ended with a final comparison questionnaire and focus group discussions. Furthermore, the students’ micro notes and their feedback were analysed to investigate the implications of mobile micro note taking. Analysis of the data provided insight into issues related to students’ note taking activity, as well as an evaluation of students’ experiences and the perceived usefulness of note taking using a micro note taking mobile application. Additionally, the research findings showed that using the developed mobile micro note taking positively supported the students’ experience and perceived usefulness of the practice of note taking. Future research directions and recommendations are discussed at the end of this research

"Factors Influencing Academics’ Use of Microblogging in Higher Education"

Twitter is one of the most widely used social media tools, increasingly the object of academic research but also in use by academics themselves in their daily professional practice (Focus, 2010; Gerber, 2012; Lupton, 2014; Rowlands, Nicholas, Russell, Canty, & Watkinson, 2011). A number of empirical studies have been conducted to identify the uses and benefits of Twitter by scholars, at a general level. Among its core benefits appear to be that it offers a professional and scientific conversation channel, a means for sharing research ideas and increased research visibility, bridging geographical distances among academics community and practitioners; the facilitation of global partnerships in research; augmentation of teaching and learning; and the strengthening of academics’ engagement with public audiences, enhancing academic esteem and self-promotion (Lupton, 2014; Pearce, Weller, Scanlon, & Kinsley, 2010; Veletsianos & Kimmons, 2012a; Veletsianos, 2013). However, there has been little qualitative research on how academics practice Twitter (Kieslinger, Ebner, & Wiesenhofer, 2011; Lupton, 2014; Veletsianos, 2011, 2013). In this context, the aim of the study was to explore academics’ adoption and use of Twitter in UK Higher Education and the factors that influence their use of it. The study employed a qualitative method within an interpretive methodology (Mason, 2002; Miles & Huberman, 1994). A semi-structured interview was the main method of data collection; complemented by digital observation and interview observation. A total of 28 academics from five faculties at The University of Sheffield (UoS) were interviewed. A thematic approach was taken to data analysis (Braun & Clarke, 2006). Findings captured detailed trajectories of academics’ Twitter use and six main themes emerged in the findings, namely: (1) the characteristics of Twitter users, (2) immediate drivers to adopt Twitter, (3) the pattern of adoption, (4) the range of Twitter uses, (5) temporal and behavioural patterns of Twitter use and (6) academic concerns over using Twitter. In addition, the study explores how attributes of the platform and technology affordances have key roles in shaping the practice. The study found that academics’ participation on Twitter is complex and multifaceted. Academics engage with Twitter for different purposes mainly in pursuit of academic interests and not for personal use. Findings identified nine types of Twitter use namely: (1) communication; (2) dissemination; (3) pedagogical activities; (4) building relationships and maintaining networks; (5) performing digital identity; (6) taking micro-breaks; (7) information seeking and gathering; (8) learning and (9) coordinating or amplifying other social media and website use. They perform these activities in strategic ways through a certain routines and develop approaches in managing its use. However, there is no simple formula to carrying out these activities. From a broader perspective, this study recognised two different views of the academic experience in relation to technology that could be relevant also to microblogging: a pessimistic and an optimistic view. Twitter use reflects issues identified by pessimistic commentators relating to the challenges faced by modern academics, such as: increasing competition to produce more quality and ‘impactful’ research; an agenda of excellence in teaching; pressure for public engagement; the rise of the academic ‘portfolio CV’; the research excellence framework (REF); and the wider effects of globalisation and the neoliberalism agenda (Henkel 2005; Clegg et al. 2003; Selwyn 2007; Fanghanel & Trowler 2008; Fanghanel 2011; Clegg 2012; Lorenz 2015). All these could be thought to affect how microblogging is taken up. On balance however, the experience of academics reflected more optimistic views of the impact of technology in Higher Education (Kirkup, 2010; Pearce et al., 2010; Scanlon, 2014; Veletsianos & Kimmons, 2012c; Veletsianos, 2013; M. Weller, 2011). Interviewees saw themselves as innovators and use Twitter as a vehicle to respond to the heavy workload that burdens them and they found the tools support their work in convenient and effective ways. The research makes a number of practical recommendations, providing suggestions to stakeholders in higher education such as institutions, academics and software developers. These include recommendations to provide staff with social media awareness training, promoting policies and guidelines for effective use for academics work including teaching activity, fostering take up through ‘key evangelist’ and promotional activities, offering helpdesk support, and teaching staff to anticipate risks such as managing social etiquette on Twitter. From a technical perspective, the study could inform the future design of technologies to support academic work