An investigation of user perceptions and attitudes towards learning objects

This study empirically evaluates the technology acceptance model drawn from Information Systems (IS) literature to investigate how user beliefs and attitudes influence learning-object use among higher education learners by evaluating the relationships between perceived usefulness, perceived ease of use, attitude, behavioural intentions and actual use. In the study, 601 potential learningobject users were presented with an introductory demonstration of learning objects for a Digital Systems course. Following the demonstration and practice, data on user beliefs, attitudes and intention to use learning objects were gathered, while data on actual use of learning objects was collected at the end of the semester. Subjects with prior experience using the learning objects were eliminated from further analysis, resulting in a final sample of 481 users. structural equation modelling was employed to test the hypothesised study model. The analysis showed that both the user beliefs and attitudes have significant positive relationships with behavioural intention and that behavioural intention accurately predicted the actual use of learning objects. The results extend the validity of the TAM into a learning object context and clearly pointed out that it can be used to predict users’ future behaviour. Introduction Computer and Internet technologies, in particular the World Wide Web, provide educators and learners with an innovative learning environment to stimulate and enhance the teaching and learning process. The increased use of new educational technologies such as learning object technology in higher education institutions (Bannan-Ritland, Dabbagh & Murphy, 2000; Bratina, Hayes & Blumsack, 2002; Wiley, 2001) has made user acceptance an increasingly critical issue, as the end users are crucial for the effective use of the information technologies (Cheney & Dickson, 1982). British Journal of Educational Technology Vol 39 No 4 2008 685–699 doi:10.1111/j.1467-8535.2007.00770.x © 2007 The Authors. Journal compilation © 2007 British Educational Communications and Technology Agency. Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA. Although user acceptance has received fairly extensive attention in prior research, the majority of these studies have focused on specific information systems in Management Information Systems (MIS) fields, other than education. Some examples are Internetbased systems (Adams, Nelson & Todd, 1992; Gefen & Straub, 1997; Koufaris, 2002), office applications (Davis, Bagozzi & Warshaw, 1989); Mathieson, 1991; Szajna, 1996) and telemedicine technology (Hu, Chau, Liu Sheng & Tam, 1999). There is scarce research literature that addresses learner intention to use, and acceptance of educational technologies, especially in a learning-object context.The measurement of the user perception (McMahon, Gardner, Gray & Mulhern, 1999) and an understanding the factors that promote the effective use of systems (Mun & Hwang, 2003) become increasingly important to enhance our understanding and prediction of the acceptance and utilisation of educational technologies. This study used the technology acceptance model (TAM) as the baseline model to predict the likely usage of learning objects as supplementary learning resources for traditional face-to-face classes in a higher-education context. To accomplish the purpose, structure equation modelling was employed to examine and validate the hypothesised relationships of user perceptions and attitudes towards the behavioural intention to use and actual use of learning objects. Theoretical background Information technology has been widely implemented in education to augment traditional face-to-face teaching and learning (Ho, Savenye & Haas, 1986; Reader & Hammond, 1994). In an attempt to make the instructional resources more efficient and to meet the diverse needs of learners, many organisations and institutions have been investing considerable amounts of financial resources for the integration and utilisation of learning objects into their e-learning systems (Urden & Weggen, 2000). The Institute of Electrical and Electronics Engineers (IEEE) formed the Learning Technology Standards Committee (LTSC) to create the common standards for the description, interchange and management of learning objects (LTSC, 2002). The goal is to develop an open architecture for online learning that will allow teaching to be centred around the needs of the learner and to allow for greater customisation and flexibility in the learning environment. This concept has since filtered into the broader field of education (Bannan-Ritland et al, 2000; Bratina et al, 2002; Wiley, 2001), with more and more instructional content being developed as learning objects because of their potential for reusability, interoperability, discoverability and manageability (Singh, 2000). Like any other trend involving information systems, this has made user acceptance an increasingly critical issue, as the success of the information systems largely depends on user satisfaction and acceptance (Bharati, 2003; DeLone & McLean, 1992; Doll & Torkzadeh, 1992); Seddon, 1997). Stokes (2001) indicated that the issue of learner satisfaction and acceptance in the digital environment is very important. A high level of learner satisfaction and acceptance reflects that the users are more willing to continue to use the technology (Biner, Dean & Mellinger, 1994; Chute, Thompson & Hancock, 1999; Tallman, 1994). 686 British Journal of Educational Technology Vol 39 No 4 2008 © 2007 The Authors. Journal compilation © 2007 British Educational Communications and Technology Agency. Several intention-based theories and models have been proposed and empirically tested in the last decade in understanding user adoption and usage of information technology (IT) innovations. For example, the theory of reasoned action—TRA (Fishbein & Ajzen, 1975), TAM (Davis et al, 1989), the theory of planned behaviour (Ajzen, 1991), innovation diffusion theory (Roger, 1995), and the Information Systems (IS) success model (DeLone & McLean, 1992). Those frameworks have been applied to a variety of information technologies in different contexts and populations (Gefen, Karahanna & Straub, 2003; Hassan, 2003; Ong, Lai & Wang, 2004; Saade & Bahli, 2005; Tetiwat & Huff, 2002; Venkatesh, Speier & Morris, 2002). Among them, the TAM (Davis, 1986) is one of the most influential and frequently tested models, and is widely applied to explain general IT adoption in the IS literature (Jong-Ae, 2005; Ma & Liu, 2004; Saga & Zmud, 1994). The TAM is a specific model developed to explain and predict users’ computer usage behaviour. Derived from the TRA (Ajzen & Fishbein, 1980; Fishbein & Ajzen, 1975), it predicts user acceptance based on the influence of two user beliefs: perceived usefulness (PU) and perceived ease of use (PEU). Both PU and PEU are posited as having significant impact on a user’s attitude (AT) towards using the system. Behavioural intentions (BI) to use is jointly determined by a person’s attitude towards using the system and its perceived usefulness. BI then determines the actual use (AU) of the system. Using different methodologies, numerous studies have found that PU and PEU correlate well with IT acceptance across a wide range of information systems (Gefen et al, 2003; Hassan, 2003; Ong et al, 2004; Saade & Bahli, 2005; Tetiwat & Huff, 2002; Venkatesh et al, 2002). Likewise, empirical research has also shown that BI is the strongest predictor of AU (Fred. D. Davis et al, 1989; Taylor & Todd, 1995). Research model and hypotheses In this study, the TAM was used as the baseline model to verify the following hypothesised relationships in the context of learning-objects usage among higher educational learners. Figure 1 shows the studied model that posits that behavioural intention determines actual use and behavioural intention is determined by both attitude and perceived usefulness. Perceived usefulness and perceived ease of use both have direct relationships with attitude. Perceived ease of use also influences perceived usefulness. This study model involves testing of six hypotheses as shown in Figure 1. Behavioural intention to use Actual usage Attitude towards using Perceived usefulness Perceived ease of use H2(+) H1(+) H4(+)