Learning with mobile technologies Students behavior

doi:10.1016/J.CHB.2016.05.027

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Learning with mobile technologies Students behavior

Journal Article•DOI•

Learning with mobile technologies Students behavior

Laura Briz-Ponce¹, Anabela Pereira², Lina Carvalho³, Juan Antonio Juanes-Mndez¹, Francisco Jos Garca-Pealvo¹ - Show less +1 more•Institutions (3)

University of Salamanca¹, University of Aveiro², University of Coimbra³

01 Jul 2017-Computers in Human Behavior (Pergamon)-Vol. 72, pp 612-620

TL;DR: The factors that affect the students behaviour towards the use of mobile technologies are analyzed to provide support for the Technology Acceptance Model and the implications are discussed within the context of Innovation in Education.

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About: This article is published in Computers in Human Behavior.The article was published on 2017-07-01. It has received 273 citations till now. The article focuses on the topics: Unified theory of acceptance and use of technology & Technology acceptance model.

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Book Chapter•DOI•

____ : ..... '"i-t.-

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U.K. Chatterjee, S.K. Bose, S.K. Roy

02 Mar 2001

984 citations

Journal Article•DOI•

Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model.

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Cheng-Min Chao¹•Institutions (1)

National Taichung University of Science and Technology¹

16 Jul 2019-Frontiers in Psychology

TL;DR: The behavioral intention to use m-learning from the perspective of consumers was explored by applying the extended unified theory of acceptance and use of technology (UTAUT) model with the addition of perceived enjoyment, mobile self-efficacy, satisfaction, trust, and perceived risk moderators.

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Abstract: This study developed and empirically tested a model to predict the factors affecting students' behavioral intentions toward using mobile learning (m-learning). This study explored the behavioral intention to use m-learning from the perspective of consumers by applying the extended unified theory of acceptance and use of technology (UTAUT) model with the addition of perceived enjoyment, mobile self-efficacy, satisfaction, trust, and perceived risk moderators. A cross-sectional study was conducted by employing a research model based on multiple technology acceptance theories. Data were derived from an online survey with 1,562 respondents and analyzed using structural equation modeling. Partial least squares (PLS) regression was used for model and hypothesis testing. The results revealed that (1) behavioral intention was significantly and positively influenced by satisfaction, trust, performance expectancy, and effort expectancy; (2) perceived enjoyment, performance expectancy, and effort expectancy had positive associations with behavioral intention; (3) mobile self-efficacy had a significantly positive effect on perceived enjoyment; and (4) perceived risk had a significantly negative moderating effect on the relationship between performance expectancy and behavioral intention. Our findings correspond with the UTAUT model and provide a practical reference for educational institutions and decision-makers involved in designing m-learning for implementation in universities.

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382 citations

Journal Article•DOI•

Technology Acceptance Model in M-learning context: A systematic review

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Mostafa Al-Emran¹, Vitaliy Mezhuyev¹, Adzhar Kamaludin¹•Institutions (1)

Universiti Malaysia Pahang¹

01 Oct 2018-Computers in Education

TL;DR: The main findings include that most of the TAM studies involving M-learning focused on extending the TAM with external variables, followed by the studies that extended the model by factors from other theories/models.

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Abstract: Various review studies were conducted to provide valuable insights into the current research trend of the Technology Acceptance Model (TAM). Nevertheless, this issue still needs to be investigated from further directions. It has been noticed that research overlooks the investigation of TAM with regard to Mobile learning (M-learning) studies from the standpoint of different perspectives. The present study systematically reviews and synthesizes the TAM studies related to M-learning aiming to provide a comprehensive analysis of 87 research articles from 2006 to 2018. The main findings include that most of the TAM studies involving M-learning focused on extending the TAM with external variables, followed by the studies that extended the model by factors from other theories/models. In addition, the main research problem that was frequently tackled among all the analyzed studies was to examine the acceptance of M-learning among students. Moreover, questionnaire surveys were the primarily relied research methods for data collection. Additionally, most of the analyzed studies were undertaken in Taiwan, this is followed by Spain, China, and Malaysia, respectively among the other countries. Besides, most of the analyzed studies were frequently conducted in humanities and educational context, followed by IT and computer science context, respectively among the other contexts. Most of the analyzed studies were carried out in the higher educational settings. To that end, the findings of this review study provide an insight into the current trend of TAM research involving M-learning studies and form an essential reference for scholars in the M-learning context.

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290 citations

Journal Article•DOI•

Mobile-based assessment: integrating acceptance and motivational factors into a combined model of self-determination theory and technology acceptance

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Stavros A. Nikou¹, Anastasios A. Economides¹•Institutions (1)

University of Macedonia¹

01 Mar 2017-Computers in Human Behavior

TL;DR: The proposed Mobile Based Assessment - Motivational and Acceptance Model (MBA-MAM), a combined model that explains and predicts Behavioral Intention to Use Mobile-based Assessment, is proposed, explaining and predicting students’ intention to use MBA in terms of both acceptance and motivational factors.

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223 citations

Cites background from "Learning with mobile technologies S..."

...Furthermore, many external variables have been added so far to predict and explain behavioral intention to use mobile learning: performance expectancy, effort expectancy, social influence, perceived playfulness (Wang, Wu, & Wang, 2009), facilitating conditions (Iqbal & Qureshi, 2012), quality of service and personal innovativeness (Abu-Al-Aish & Love, 2013), ICT literacy and anxiety (Mac Callum, Jeffrey, & Kinshuk, 2014), social influence (Briz-Ponce, Pereira, Carvalho, Juanes-M endez, & GarcíaPe~ nalvo, 2016). Based on a model of self-determination theory in online learning proposed by Chen and Jang (2010), online learning environments, including m-learning, require and support at the same time the following features: flexibility and choice, employment of technical skills and social interactions. These features are perfectly aligned to the basic constructs of SDT respectively: autonomy, competence and relatedness. Furthermore, the pedagogical framework of mobile learning proposed by Burden and Kearney (2016) highlights three distinctive features of m-learning: personalization, collaboration and authenticity....
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...Furthermore, many external variables have been added so far to predict and explain behavioral intention to use mobile learning: performance expectancy, effort expectancy, social influence, perceived playfulness (Wang, Wu, & Wang, 2009), facilitating conditions (Iqbal & Qureshi, 2012), quality of service and personal innovativeness (Abu-Al-Aish & Love, 2013), ICT literacy and anxiety (Mac Callum, Jeffrey, & Kinshuk, 2014), social influence (Briz-Ponce, Pereira, Carvalho, Juanes-M endez, & GarcíaPe~ nalvo, 2016). Based on a model of self-determination theory in online learning proposed by Chen and Jang (2010), online learning environments, including m-learning, require and support at the same time the following features: flexibility and choice, employment of technical skills and social interactions....
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Journal Article•DOI•

Mobile-based assessment

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Stavros A. Nikou¹, Anastasios A. Economides¹•Institutions (1)

University of Macedonia¹

01 Jun 2017-Computer Education

TL;DR: The study investigates factors influencing students' adoption of mobile-based assessment, and introduces the proposed Mobile-Based Assessment Acceptance Model (MBAAM), which explains about 47% of the total variance in Behavioral Intention to Use.

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Abstract: Acceptance and intention to use mobile learning is a topic of growing interest in the field of education. Although there is a considerable amount of studies investigating mobile learning acceptance, little research exists that investigates the driving factors that influence students' intention to use mobile technologies for assessment purposes. The aim of this study is to provide empirical evidence on the acceptance of Mobile-Based Assessment (MBA), the assessment delivered through mobile devices and technologies. The proposed model, Mobile-Based Assessment Acceptance Model (MBAAM) is based on the Technology Acceptance Model (TAM). MBAAM extends TAM in the context of MBA by adding to the Perceived Ease of Use and Perceived Usefulness, the constructs of Facilitating Conditions, Social Influence, Mobile Device Anxiety, Personal Innovativeness, Mobile-Self-Efficacy, Perceived Trust, Content, Cognitive Feedback, User Interface and Perceived Ubiquity Value and investigates their impact on the Behavioral Intention to Use MBA. 145 students from a European senior-level secondary school experienced a series of mobile-based assessments for a three-week period. Structured equation modeling was used to analyze quantitative survey data. According to the results, MBAAM explains and predicts approximately 47% of the variance of Behavioral Intention to Use Mobile-Based Assessment. The study provides a better understanding towards developing mobile-based assessments that support learners, enhance learning experience and promote learning, taking advantage of the distinguished features that mobile devices may offer. Implications are discussed within the wider context of mobile learning acceptance research. The study investigates factors influencing students' adoption of mobile-based assessment.The study introduces Mobile-Based Assessment Acceptance Model (MBAAM).MBAAM explains about 47% of the total variance in Behavioral Intention to Use.

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219 citations

Cites background from "Learning with mobile technologies S..."

...The acceptance and adoption of mobile learning is a topic of growing interest in the field of education and it is still evolving (Briz-Ponce, Pereira, Carvalho, Juanes-M endez, & García-Pe~nalvo, 2016; Cheon, Lee, Crooks, & Song, 2012; Liu, Han, & Li, 2010)....
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...Research has shown that perceived anxiety is negatively related to Perceived Ease of Use (Liaw & Huang, 2015; S anchez-Prieto, Olmos-Miguel a~nez, & García-Pe~nalvo, 2016)....
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References

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Open Access

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Journal Article•DOI•

Evaluating Structural Equation Models with Unobservable Variables and Measurement Error

[...]

Claes Fornell¹, David F. Larcker²•Institutions (2)

University of Michigan¹, Northwestern University²

01 Feb 1981-Journal of Marketing Research

TL;DR: In this paper, the statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined, and a drawback of the commonly applied chi square test, in additit...

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Abstract: The statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined. A drawback of the commonly applied chi square test, in addit...

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56,555 citations

"Learning with mobile technologies S..." refers methods in this paper

...For the reliability, this study follows the criteria suggested by Fornell and Larcker (1981), Chin (1998) and Hair, Hult, Ringle and Sarstedt (2016)....
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Book•

Principles and Practice of Structural Equation Modeling

[...]

Rex B. Kline¹•Institutions (1)

Concordia University Wisconsin¹

27 May 1998

TL;DR: The book aims to provide the skills necessary to begin to use SEM in research and to interpret and critique the use of method by others.

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Abstract: Designed for students and researchers without an extensive quantitative background, this book offers an informative guide to the application, interpretation and pitfalls of structural equation modelling (SEM) in the social sciences. The book covers introductory techniques including path analysis and confirmatory factor analysis, and provides an overview of more advanced methods such as the evaluation of non-linear effects, the analysis of means in convariance structure models, and latent growth models for longitudinal data. Providing examples from various disciplines to illustrate all aspects of SEM, the book offers clear instructions on the preparation and screening of data, common mistakes to avoid and widely used software programs (Amos, EQS and LISREL). The book aims to provide the skills necessary to begin to use SEM in research and to interpret and critique the use of method by others.

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42,102 citations

Perceived Usefulness, Perceived Ease of Use, and User

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Perceived Usefulness, Fred D. Davis

01 Jan 1989

TL;DR: Regression analyses suggest that perceived ease of use may actually be a causal antecdent to perceived usefulness, as opposed to a parallel, direct determinant of system usage.

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40,975 citations

"Learning with mobile technologies S..." refers methods or result in this paper

...Also, the Perceived Ease of Use (PEOU) has a positive impact on the user’s attitude, which also agrees with the results of the Davis’ study (Davis et al., 1989)....
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...The second section included 34 items and was designed based on the TAM published by Davis (Davis, 1989) and the constructs reported by other article published in order to unify the different versions of the model (Venkatesh, Morris, Davis, & Davis, 2003)....
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...Another important contribution to highlight is the introduction of Social Influence (SI) as an important factor to affect the user’s attitude (ATU), whereas other author revealed that this factor affects directly the Behavioural Intention (Venkatesh, Thong, & Xu, 2012)....
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...According to the results, the variable that accounts for most of the variance in ATU, BI and RELREC are PU, RELREC and ATU respectively....
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...Also, a study about mobile learning in Higher education, performed in three important Chinese universities (Zhu, Guo,&Hu, 2012), reported that Perceived Usefulness (PU) exerts more Please cite this article in press as: Briz-Ponce, L., et al., Learning with mob (2016), http://dx.doi.org/10.1016/j.chb.2016.05.027 influence on user’s attitude (ATU) than Perceived ease of use (PEOU)....
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Journal Article•DOI•

Perceived usefulness, perceived ease of use, and user acceptance of information technology

[...]

Fred D. Davis¹•Institutions (1)

University of Michigan¹

01 Sep 1989-Management Information Systems Quarterly

TL;DR: In this article, the authors developed and validated new scales for two specific variables, perceived usefulness and perceived ease of use, which are hypothesized to be fundamental determinants of user acceptance.

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Abstract: Valid measurement scales for predicting user acceptance of computers are in short supply. Most subjective measures used in practice are unvalidated, and their relationship to system usage is unknown. The present research develops and validates new scales for two specific variables, perceived usefulness and perceived ease of use, which are hypothesized to be fundamental determinants of user acceptance. Definitions of these two variables were used to develop scale items that were pretested for content validity and then tested for reliability and construct validity in two studies involving a total of 152 users and four application programs. The measures were refined and streamlined, resulting in two six-item scales with reliabilities of .98 for usefulness and .94 for ease of use. The scales exhibited hgih convergent, discriminant, and factorial validity. Perceived usefulness was significnatly correlated with both self-reported current usage r = .63, Study 1) and self-predicted future usage r = .85, Study 2). Perceived ease of use was also significantly correlated with current usage r = .45, Study 1) and future usage r = .59, Study 2). In both studies, usefulness had a signficnatly greater correaltion with usage behavior than did ease of use. Regression analyses suggest that perceived ease of use may actually be a causal antecdent to perceived usefulness, as opposed to a parallel, direct determinant of system usage. Implications are drawn for future research on user acceptance.

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40,720 citations

Journal Article•DOI•

A power primer.

[...]

Jacob Cohen¹•Institutions (1)

York University¹

01 Jul 1992-Psychological Bulletin

TL;DR: A convenient, although not comprehensive, presentation of required sample sizes is providedHere the sample sizes necessary for .80 power to detect effects at these levels are tabled for eight standard statistical tests.

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Abstract: One possible reason for the continued neglect of statistical power analysis in research in the behavioral sciences is the inaccessibility of or difficulty with the standard material. A convenient, although not comprehensive, presentation of required sample sizes is provided here. Effect-size indexes and conventional values for these are given for operationally defined small, medium, and large effects. The sample sizes necessary for .80 power to detect effects at these levels are tabled for eight standard statistical tests: (a) the difference between independent means, (b) the significance of a product-moment correlation, (c) the difference between independent rs, (d) the sign test, (e) the difference between independent proportions, (f) chi-square tests for goodness of fit and contingency tables, (g) one-way analysis of variance, and (h) the significance of a multiple or multiple partial correlation.

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38,291 citations

"Learning with mobile technologies S..." refers background in this paper

...35 indicates small, medium and large effect (Cohen, 1992)....
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...In this case, the effect size of 0.02, 0.15 and 0.35 indicates small, medium and large effect (Cohen, 1992)....
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