Factors influencing beliefs for adoption of a learning analytics tool: An empirical study

Present research and development offer various learning analytics tools providing insights into different aspects of learning processes. Adoption of a specific tool for practice is based on how its learning analytics are perceived by educators to support their pedagogical and organizational goals. In this paper, we propose and empirically validate a Learning Analytics Acceptance Model (LAAM) of factors influencing the beliefs of educators concerning the adoption a learning analytics tool. In particular, our model explains how the usage beliefs (i.e., ease-of-use and usefulness perceptions) about the learning analytics of a tool are associated with the intention to adopt the tool. In our study, we considered several factors that could potentially affect the adoption beliefs: i) pedagogical knowledge and information design skills of educators; ii) educators' perceived utility of a learning analytics tool; and iii) educators' perceived ease-of-use of a learning analytics tool. By following the principles of Technology Acceptance Model, the study is done with a sample of educators who experimented with a LOCO-Analyst tool. Our study also determined specific analytics types that are primary antecedence of perceived usefulness (concept comprehension and social interaction) and ease-of-use (interactive visualization).     

Web 3.0 – Based personalisation of learning objects in virtual learning environments

The paper aims at research on Web 3.0 – based personalisation of learning objects (LOs) while learning in virtual learning environments. Learning personalisation is analysed in terms of suitability of LOs and VLEs to particular learning styles. The novel sets portrait analysing interconnections between students’ learning styles, their preferred learning activities, relevant teaching/learning methods, and LOs types is presented in more detail. The well-known standardised vocabularies of teaching/learning methods and LOs types were used to establish these interconnections. The sets portrait of these interconnections is followed by the appropriate ontology. The ontology is considered as an appropriate tool to create learners’ personalised learning environments consisting of LOs, suitable teaching/learning methods and activities according to their preferred learning styles. The ontology should help the learner to find suitable LOs according to preferred learning methods/activities, and vice versa, and thus to personalise learning. The presented Web 3.0 – based approaches are the typical cases of information and communication technologies (ICT’s) application for development of human capital.     

Recommending suitable learning paths according to learners’ preferences: Experimental research results

The paper deals with the problem of personalising learning units with the main focus on finding personalised learning paths in learning units. Finding suitable learning paths is based on students’ needs in terms of their learning styles. It has been shown that learning path in static and dynamic learning units can be selected by applying artificial intelligence techniques, e.g. a swarm intelligence model, mainly by adapting ant colony optimisation method based on collaboration and pheromones. In the paper, experimental results of applying the proposed approach in practise are presented. The results of empirical experiment have shown that learning in the proposed prototype of e-learning system applying created recommending method improves students’ learning results and saves their learning time. This fact indicates that the developed adaptive method for personalising learning units is practically applicable in e-learning and enhances the learning quality.     

The acceptance and use of a virtual learning environment in China

The success of a virtual learning environment (VLE) depends to a considerable extent on student acceptance and use of such an e-learning system. After critically assessing models of technology adoption, including the Technology Acceptance Model (TAM), TAM2, and the Unified Theory of Acceptance and Usage of Technology (UTAUT), we build a conceptual model to explain the differences between individual students in the level of acceptance and use of a VLE. This model extends TAM2 and includes subjective norm, personal innovativeness in the domain of information technology, and computer anxiety. Data were collected from 45 Chinese participants in an Executive MBA program. After performing satisfactory reliability and validity checks, the structural model was tested with the use of PLS. Results indicate that perceived usefulness has a direct effect on VLE use. Perceived ease of use and subjective norm have only indirect effects via perceived usefulness. Both personal innovativeness and computer anxiety have direct effects on perceived ease of use only. Implications are that program managers in education should not only concern themselves with basic system design but also explicitly address individual differences between VLE users.     

An empirical investigation of student acceptance of course websites

The World Wide Web (WWW) is the future in teaching and learning. This paper uses the Technology Acceptance Model (TAM) constructs of usefulness and ease of use to assess university students’ acceptance of course websites as an effective learning tool. A survey instrument was distributed to 450 undergraduate students and a total of 403 usable responses were obtained. Exploratory and confirmatory factor analyses were implemented using structural equation modeling techniques through LISREL version 8.52. A structural equation model was used to fit and validate the Course Website Acceptance Model (CWAM) and the results indicated good fit to the data. Course website usefulness and ease of use proved to be key determinants of the acceptance and usage of course website as an effective and efficient learning technology. The causal relationships between the constructs considered by the CWAM were well supported, accounting for 83% of the total variance in the course website acceptance and usage.     

Behavioral intention,use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning

Widespread implementation of e-learning systems – learning management systems, virtual learning environments – across higher education institutions has aroused great interest on the study of e-learning acceptance. Acceptance studies focus on the predictors of system adoption and use, with behavioral intention to use the system as a proxy for actual use. This study proposes a TAM3-based model – with the inclusion of two additional variables: personal innovativeness in the domain of information technology and perceived interaction – to study the factors influencing the acceptance of e-learning systems. Attention is also brought towards the role of behavioral intention, especially in its relation with use behavior. In order to do so, two different settings were considered: higher education and lifelong learning; data was gathered from a survey administrated to Spanish graduate and lifelong learning students, and partial least squares analysis was used to test the research model. Results supported TAM relations, except for the intention-behavior linkage, and unveiled a dual nature of perceived usefulness – with one component related to efficiency and performance, and another component related to flexibility. The adequacy of applying TAM3-based models in educational contexts and suitability of actual system usage measures are also discussed.     

The effects of education compatibility and technological expectancy on e-learning acceptance

Discerning what influences a student’s acceptance of e-learning is still unclear and has not been well investigated. On the basis of the expectancy-value theory, much effort has been put into identifying the effectual factors regarding the technological expectancy of students. However, aside from technological usage, the adoption of an e-learning system still must consider learning behavior. Thus, researchers should take into consideration both technological and learning expectancies of students while investigating e-learning acceptance. Following mainstream literature on information system acceptance, this study postulates that a student’s behavioral intention to accept an e-learning system is determined both by his or her technological expectancy and educational compatibility. Four primary factors, that is, performance expectancy, effort expectancy, social influence, and facilitating conditions, specified in the Unified Theory of Acceptance and Use of Technology (UTAUT) are used to reflect the technological expectancy of students. Further, educational compatibility, which refers the congruence of e-learning systems with the unique leaning expectancies of students, is integrated with the UTAUT to form a new theoretical model for e-learning acceptance. An empirical survey is conducted to examine the proposed model. A total of 626 valid samples were collected from the users of an e-learning system. The findings show that both technological expectancy and educational compatibility are important determinants of e-learning acceptance. However, educational compatibility reveals a greater total effect on e-learning acceptance than does technological expectancy. Implications and practical guidelines for both e-learning developers and practitioners are subsequently presented.     

Applying learning analytics dashboards based on process-oriented feedback to improve students' learning effectiveness

With the development of a technology-supported environment, it is plausible to provide rich process-oriented feedback in a timely manner. In this paper, we developed a learning analytics dashboard (LAD) based on process-oriented feedback in iTutor to offer learners their final scores, sub-scale reports, and corresponding suggestions on further learning content. We adopted a quasi-experimental design to investigate the effectiveness of the report on students' learning. Ninety-four freshman from two classes participated in this research. The two classes were divided into the LAD group and the original analytics report (OAR) based on a product-oriented feedback group. Before the experiment, all the students took the prior knowledge assessment. After a semester's instruction, all the students took the post-test of the summative assessment. Results indicated that students in the LAD group experienced better learning effectiveness than students in the OAR group. LAD based on process-oriented feedback was also effective in improving the skill learning effectiveness of the students with low-level prior knowledge.     

Adapting the Technology Acceptance Model to evaluate the innovative potential of e-learning systems

This paper describes an experience where the Technology Acceptance Model (TAM) has been adapted for use in the evaluation of methodological and technological innovations determined by the introduction of a new e-learning system in an Italian online university. While the original TAM allows one to assess acceptance and adoption of a new technology, in this case there was also a need to consider all the phases of use of the system (course design, running and evaluation), all the users of the system (students, teachers and e-learning management), and all the system’s components (the e-learning platform, the learning resources and mostly the underlying pedagogical approach). The resulting model, which is an extension of the original TAM, is a three-dimensional one, with three aspects to be considered on each axis (phases of use, users and components). For each of the 27 combinations of these aspects, indicators of usefulness and ease-of-use have been identified. When available, data concerning actual use (derived from the tracking functions of the platform) and effectiveness (based on teachers’ adoption of new tools and students’ learning outcomes) have also been used to complement the data.     

Learning management systems and cloud file hosting services: A study on students’ acceptance

The aim of this paper is to investigate the motivations that lead higher education students to replace several Learning Management Systems (LMS) services with cloud file hosting services for information sharing and collaboration among them. The research approach is based on the Technology Acceptance Model (TAM). More specifically, the model is devoted to identifying barriers and enablers to the acceptance of these technologies. A questionnaire comprising three factors (Attitude toward using technology, Perceived ease of use and Perceived usefulness) was applied to a sample consisting of 121 higher education students. Results show that the perceived ease of use of cloud file hosting services is above that of LMS tools and services and that cloud file hosting services presented higher levels of perceived usefulness than standard learning management tools. In addition, attitude toward using cloud file hosting services is well above that of using LMS tools.     

Human acceptance evaluation of AR-assisted assembly scenarios

The contribution examines the acceptance of Augmented Reality (AR) in assembly scenarios by a model-based approach for acceptance evaluation. After a critical literature research and analysis, a proprietary model for acceptance measurement is developed, which includes and synthesizes previous models and simplifies them considerably for the purpose of industrial assembly. Consequently, a structural model is derived which is based on the basic concepts of the Unified Theory of Acceptance and Use of Technology (UTAUT) and Technology Acceptance Model (TAM). This is built up as a path model to explain user acceptance. Following, a laboratory experiment is set up to collect data. At the final assembly workstation of the FHWS c-Factory the study participants assemble a toy truck once without and once with AR support. The c-Factory is a concept factory for smart production in an IoT environment. Afterwards, relevant data are collected by means of a survey. The evaluation shows that the acceptance of AR is given and the model is resilient. The results show also, that AR is accepted by the participants supporting their work.     

Investigating the influence of the most commonly used external variables of TAM on students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios

Engagement with e-portfolios has been shown to improve students’ learning. However, what influences students to accept e-portfolios is a question that needs careful study. The purpose of this study is to investigate the influence of Self-Efficacy, Subjective Norm, Enjoyment, Computer Anxiety and Experience on students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of an e-portfolio system and their Behavioural Intention (BI) to use the system for learning. To do this, the study tested and used the General Extended Technology Acceptance Model for E-Learning (GETAMEL) in the context of e-portfolios. Valid data were collected from 242 UK undergraduate students who had been introduced to e-portfolios. The data set was analysed using SPSS software. Results showed that the best predictor of student’s Perceived Ease of Use of the e-portfolio is Experience, followed by Enjoyment, Self-Efficacy and Subjective Norm. The best predictor of student’s Perceived Usefulness of the e-portfolio is Perceived Ease of Use followed by Enjoyment. Both Perceived Ease of Use and Perceived Usefulness predict student’s Behavioural Intention to Use the e-portfolio. The findings improve understanding regarding acceptance of e-portfolio systems and this work is therefore of particular interest to researchers, developers and practitioners of e-portfolios.     

Supervised machine learning in multimodal learning analytics for estimating success in project‐based learning

Multimodal learning analytics provides researchers new tools and techniques to capture different types of data from complex learning activities in dynamic learning environments. This paper investigates the use of diverse sensors, including computer vision, user‐generated content, and data from the learning objects (physical computing components), to record high‐fidelity synchronised multimodal recordings of small groups of learners interacting. We processed and extracted different aspects of the students' interactions to answer the following question: Which features of student group work are good predictors of team success in open‐ended tasks with physical computing? To answer this question, we have explored different supervised machine learning approaches (traditional and deep learning techniques) to analyse the data coming from multiple sources. The results illustrate that state‐of‐the‐art computational techniques can be used to generate insights into the "black box" of learning in students' project‐based activities. The features identified from the analysis show that distance between learners' hands and faces is a strong predictor of students' artefact quality, which can indicate the value of student collaboration. Our research shows that new and promising approaches such as neural networks, and more traditional regression approaches can both be used to classify multimodal learning analytics data, and both have advantages and disadvantages depending on the research questions and contexts being investigated. The work presented here is a significant contribution towards developing techniques to automatically identify the key aspects of students success in project‐based learning environments, and to ultimately help teachers provide appropriate and timely support to students in these fundamental aspects.     

PeRSIVA: An empirical evaluation method of a student model of an intelligent e-learning environment for computer programming

In this paper an evaluation method for assessing the effectiveness, accuracy and validity of a student model was presented. Our method is called PeRSIVA and is a combination of the well-known evaluation method of Kirkpatrick and the layered evaluation framework. These well-known and commonly used evaluation techniques have been selected in order to design an accurate and correct evaluation methodology, since there are no clear guidelines in the literature for the evaluation of the student model of an adaptive tutoring system. Furthermore, PeRSIVA method was used to evaluate the hybrid student model, which combines an overlay model with stereotypes and fuzzy logic techniques, of an e-learning system. Particularly, PeRSIVA assesses the results of student modeling in students' satisfaction, performance, progress, behavior and state, as well as the validity of the conclusions drawn by the student model and the validity of the adaptation decision making. The e-learning system was used by the students of a postgraduate program in the field of informatics in the University of Piraeus and the evaluation results demonstrated learning improvements in students and adaptation success to students' needs.     

Evaluation of quality and personalisation of VR/AR/MR learning systems

ABSTRACT

The paper aims to analyse the problem of quality evaluation and personalisation of virtual reality/augmented reality/mixed reality (VR/AR/MR). First of all, systematic review of relevant scientific literature on the research topic was conducted. After that, findings of the systematic review concerning evaluation of quality and personalisation of VR/AR/MR learning environments are presented. The author’s VR/AR/MR learning systems/environments quality evaluation and personalisation framework is also presented in the paper. Evaluation of quality of VR/AR/MR platforms/environments should be based on (a) applying both expert-centred (top-down) and user-centred (bottom-up) quality evaluation methods and (b) separating ‘internal quality’ criteria, and ‘quality in use’ criteria in the set of quality criteria (model). Personalisation of VR/AR/MR platforms/environments should be based on learners’ models/profiles using students’ learning styles, intelligent technologies, and Semantic Web applications.     

Collaborative learning and mobile devices: An educational experience in Primary Education

In this paper we review the relevance of the models of Collaborative Learning (CL) in Primary Education, also exploring the integration of Information and Communication Technology (ICT) in these processes. Through the case study method, the main objective of this article is to describe the design, implementation and evaluation of a collaborative experience with ICT involving students in the 5th year of Primary Education in a Spanish school. Information has been collected from all those involved: teachers, tutors, teacher ICT support and students from two classes participating in the investigation. The results confirm the fact that ICT provide tools and channels that have multiplied the possibilities to carry out collaborative projects, providing quality assurance to share and communicate. The experience presented provides evidence to confirm this, and at the same time, confronts the dominant methodology based more on individual learning where the teacher continues to exercise the role of sole transmitter of knowledge.     

Collaborative contracting for Manufacturing-as-a-Service (MaaS) by information content measurement and decision tree learning

Product realization through Manufacturing-as-a-Service (MaaS) has observed as an emerging trend towards Industry 4.0. It offers new opportunities for reaching external partner’s knowledge and resources while allowing companies to focus on their core competencies. This paper envisions the importance of collaborative contracting for MaaS fulfillments, in which tournament-based crowdsourcing entails evaluation and selection of manufacturing service providers as a best-matching problem of multi-criteria decision making. MaaS collaborative contracting involves numerous evaluation criteria related to not only technical capabilities of the fulfilling agents, but also their business performance involved with MaaS operations in the past history. This paper develops a contracting analysis and evaluation methodology for selection of appropriate MaaS agent selection. The proposed evaluation mechanism utilizes information content measure and decision tree learning for better matching of fulfilling agents to maximize customer satisfaction and effectiveness of multiple stakeholders of MaaS operations. The multi-attribute utility theory is integrated with decision tree learning within a coherent predictive analytics framework to not only synthesize pre-defined functional and cost requirements for customers, but also leverage upon historical data of business performance. A case study of orthodontic brace mass customization through MaaS is reported to illustrate the feasibility and potential of collaborative contracting in MaaS.     

Evaluation of learners’ attitude toward learning in ARIES augmented reality environments

The ARIES system for creating and presenting 3D image-based augmented reality learning environments is presented. To evaluate the attitude of learners toward learning in ARIES augmented reality environments, a questionnaire was designed based on Technology Acceptance Model (TAM) enhanced with perceived enjoyment and interface style constructs. For empirical study, a scenario of a chemistry experimental lesson was developed. The study involved students of the second grade of lower secondary school. As follows from this study, perceived usefulness and enjoyment had a comparable effect on the attitude toward using augmented reality environments. However, perceived enjoyment played a dominant role in determining the actual intention to use them. The interface style based on physical markers had significant impact on perceived ease of use. Interface style and perceived ease of use had a weak influence on perceived enjoyment. In contrast, these two constructs had a significantly stronger influence on perceived usefulness.     

The acceptance and use of computer based assessment

The effective development of a computer based assessment (CBA) depends on students’ acceptance. The purpose of this study is to build a model that demonstrates the constructs that affect students’ behavioral intention to use a CBA. The proposed model, Computer Based Assessment Acceptance Model (CBAAM) is based on previous models of technology acceptance such as Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), and the Unified Theory of Acceptance and Usage of Technology (UTAUT). Constructs from previous models were used such as Perceived Usefulness, Perceived Ease of Use, Computer Self Efficacy, Social Influence, Facilitating Conditions and Perceived Playfulness. Additionally, two new variables, Content and Goal Expectancy, were added to the proposed research model. Data were collected from 173 participants in an introductory informatics course using a survey questionnaire. Partial Least Squares (PLS) was used to test the measurement and the structural model. Results indicate that Perceived Ease of Use and Perceived Playfulness have a direct effect on CBA use. Perceived Usefulness, Computer Self Efficacy, Social Influence, Facilitating Conditions, Content and Goal Expectancy have only indirect effects. These eight variables explain approximately 50% of the variance of Behavioural Intention.     

Customers’ learning process during product customization: The case of online configuration tool kits

Online configuration tool kits present attractive opportunities for creating customized offers. The purpose of this study is to analyze the relevance of both the TAM (Technology Acceptance Model) and the experiential learning theory to understand how configuration contributes to provide value for end users. The qualitative survey shows that the TAM needs to be adapted to the case of configurators by measuring both perceived usefulness (PU) of the configured product and PU and ease of use of the configurator itself. The study shows the relevance of experiential learning theory as an antecedent of the TAM. A model summarizing our observations is proposed and discussed.