Computer Based Assessment (CBA) is being a very popular method to evaluate students’ performance at the university level. This research aims to examine the constructs that affect students’ intention to use the CBA. The proposed model is based on previous technology models such as Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), and Unified Theory of Acceptance and Usage of Technology (TAUT). The proposed CBA model is based on nine variables: Goal Expectancy, Social Influence, Facilitating Conditions, Computer Self Efficacy, Content, Perceived Usefulness, Perceived Ease of Use, Perceived Playfulness, and Behavioral Intention. Data were collected using a survey questionnaire from 546 participants who had used the computer based exam system at the University of Jordan. Results indicate that Perceived Playfulness has a direct effect on CBA use. Perceived Ease of Use, Perceived Usefulness, Computer Self Efficacy, Social Influence, Facilitating Conditions, Content and Goal Expectancy have only indirect effects. The study concludes that a system is more likely to be used by students if it is playful and CBA is more likely to be playful when it is easy to use and useful. Finally, the studied acceptance model for computer based assessment explains approximately only 10% of the variance of behavioral intention to use CBA.
Student assessment is a very essential element in any learning model. Instructors evaluate students and learning output to direct and motivate them based on their achievement [
Communications and computer technologies have been developed very quickly and it is being widespread and is used for several purposes [
CBA is being a main part of electronic learning and assessment systems in higher education institutions. Therefore, it is very essential to investigate the factors that affect the students’ attitude toward using CBA in order to implement CBA systems successfully. This research aims to examine the factors that influence the students’ attitude toward using CBA system in Jordan. Recent studies have shown that Perceived Usefulness, Perceived Ease of Use, Perceived Playfulness, and Perceived Importance each has a significant role in Behavioral Intention to use CBA [
The paper is organized as follows. In Section 2, a review of theoretical background of CBA is presented. Section 3 discusses the hypotheses development. Section 4 explains the research methodology in details. In Section 5, research results are shown. Section 6 discusses the results of collected date based on the proposed model. Finally, discussion and conclusions are drawn in Section 7.
Computer based assessment and the factors that influence students’ intention behavior have been studied insensitively in the literature. Many researchers focus on studying the effect of some influencing factors such as Perceived Usefulness, Perceived Ease of Use, and Perceived Playfulness [
In 2002, C. Jantz et al. measure and examine the effectiveness of Interactive Multimedia (IMM) using a quasi-experimental pretest/post-test [
Later on, a Web-based Educational System (WEAS) based on Bloom’s theory was introduced and tested on science courses [
(Terzis and Economides, 2011) built a model to investigate students’ intention to use Computer Based Assessment (CBA) called Computer Based Assessment Acceptance Model (CBAAM) [
(M. Alquraan, 2012) investigates different learning assessment methods used in higher education. Samples of 736 undergraduate students from four well-known universities in Jordan were engaged in the investigation process [
In 2012, conducted a study to identify how personality affects technology acceptance. It is a combination between CBAAM and Big Five Inventory Question (BFI) for the purpose of analyzing the effect of the five personality factors upon CBA’s [
A dynamic CBA system for fluid mechanics course were conducted and assessment data were collected before and after applying the system [
Another research was conducted to compare between traditional assessment and learning and educational software [
(E. Quellmalz, 2014) includes a section in chapter in the education encyclopedia which talked about assessments in the next generation of science standards, where science phenomena needs more flexible, dynamic and more complex representation [
Based on previous Technology Acceptance Models such as TAM, TPB and UTAUT, a new model called Computer Bases Assessment Acceptance Model (CBAAM) was proposed [
This model combined the following constructs to study the acceptance of a CBA:
H1: Perceived Playfulness will have a positive effect on the Behavioural Intention to use CBA.
H2: Perceived Usefulness will have a positive effect on the Behavioural Intention to use CBA.
H3: Perceived Usefulness will have a positive effect on Perceived Playfulness.
H4: Perceived Ease of Use will have a positive effect on the Behavioural Intention to use CBA.
H5: Perceived Ease of Use will have a positive effect on Perceived Usefulness.
H6: Perceived Ease of Use will have a positive effect on Perceived Playfulness.
H7: Computer Self Efficacy will have a positive effect on Perceived Ease of Use.
H8: Social Influence will have a positive effect on Perceived Usefulness.
H9: Facilitating Conditions will have a positive effect on Perceived Ease of Use.
H10: Goal Expectancy will have a positive effect on Perceived Usefulness.
H11: Goal Expectancy will have a positive effect on Perceived Playfulness.
H12: Content will have a positive effect on Perceived Usefulness.
H13: Content will have a positive effect on Perceived Playfulness.
H14: Content will have a positive effect on Goal Expectancy.
H15: Content will have a positive effect on the Behavioral Intention to Use CBA.
The following sections describe the research model constructs.
Moon and Kim (2001) extended TAM by adding the construct Perceived Playfulness [
・ Concentration: Determines whether the user is concentrated on the activity.
・ Curiosity: Determines if the system aroused the user’s cognitive curiosity [
・ Enjoyment: Determines whether the user is enjoying the interaction with the system or not.
Although the previous three dimensions are interdependent and linked, each of them alone does not reflect total interaction of users with the system. A successful implementation of a CBA is able to hold Users’ concentration, curiosity and enjoyment. Therefore, CBAAM assumed that the Behavioral Intention is positively affected by the perceived playfulness as in the following hypothesis:
H1: Perceived Playfulness will have a positive effect on the Behavioral Intention.
As mentioned before, Perceived Usefulness is used to measure how much a person believes that his/her job performance will increase when he uses a particular computer system. Many evidences were provided by researchers on the effect of Perceived Usefulness on the Behavioral Intention of users to use a learning system [
H2: Perceived Usefulness will have a positive effect on the Behavioral Intention to use CBA.
H3: Perceived Usefulness will have a positive effect on Perceived Playfulness.
It was also discussed that Perceived Ease of Use is used to measure the person’s belief that using a computer system requires no effort. Previous research showed that Perceived ease of use has a direct effect on Perceived Usefulness and Behavioral Intention [
H4: Perceived Ease of Use will have a positive effect on the Behavioral Intention to use CBA.
H5: Perceived Ease of Use will have a positive effect on Perceived Usefulness.
H6: Perceived Ease of Use will have a positive effect on Perceived Playfulness.
Research results show that there is a link between Computer Self Efficacy (CSE) and Perceived Ease of Use [
H7: Computer Self Efficacy will have a positive effect on Perceived Ease of Use.
Social Influence can be defined as the effect of people’s opinion, superior and peers influence. There are three elements that define Social Influence which are: Subjective Norm (SN), Image and Voluntariness [
In CBAAM it was assumed that Social Influence has a direct impact on Perceived Usefulness. This was concluded based on the fact that students usually feel insecure using a CBA, and they are affected by the opinion of their friends, colleagues and seniors. Also, students discuss Perceived Usefulness and its added value as the main topic regarding a CBA. The CBA in CBAAM is voluntary, so as proposed by TAM2 that it has no impact on Behavioral Intention, in CBAAM they did not study its effect on it. The only hypothesis regarding Social Influence is:
H8: Social Influence will have a positive effect on Perceived Usefulness.
Facilitating Conditions (FCs) are defined as the set of factors that affect the person’s belief to perform a procedure. There are many aspects of (FC); one of them is the technical support such as helpdesks or Online support services [
In CBAAM, FC was defined as the support that is provided during a CBA. If users face difficulties while using a CBA, support must be given to help them overcome these difficulties. This support includes having an expert to answer students’ questions and queries if the CBA is used in a university. For the previous reasons, the following hypothesis was made:
H9: Facilitating Conditions will have a positive effect on Perceived Ease of Use.
In distance learning, the need of self-direction and goal orientation was highlighted by many studies [
In CBAAM, a new construct called Goal Expectancy (GE) was introduced motivated by the previously mentioned studies. This construct defines a person’s belief that he/she is prepared well to use a CBA. GE has two aspects based on two types of assessment (summative and formative). In summative assessment (which is experimented in their study), the first dimension measures a student’s satisfaction of his/her preparation. Students have to study and prepare themselves in order to be able to answer the questions in the assessment. The second dimension measures the student’s desired success level. Each student before the assessment predicts his performance based on his/her preparation and put a percentage of correct answers as a goal that will give him satisfying performance.
It is assumed that GE highly influences Perceived Usefulness. However; this influence is dependent on the type of assessment. In Summative Assessment, GE has an impact on Usefulness because students can understand the questions and answer them. On the other hand, this is not applicable on Formative Assessment because what adds the value is the feedback provided by the CBA to enable students from understanding their learning material. Therefore, in Formative Assessment, GE has a negative impact on Perceived Usefulness as students use it to learn more than to test their knowledge.
Moreover, this model assumes that Perceived Playfulness will be positively impacted by GE. In order for students to meet their expectations of good performance they will concentrate more with the CBA, they will also be able to answer the questions correctly and will enjoy the interaction with the system more if they are well prepared. The following hypotheses are assumed:
H10: Goal Expectancy will have a positive effect on Perceived Usefulness.
H11: Goal Expectancy will have a positive effect on Perceived Playfulness.
The last construct in this model is the content. (C. Ong et al., 2004) introduced content as an important construct in learners’ satisfaction [
These dimensions of the content are proposed only in this model. Previous models examined content for different purposes. Therefore, the model assumes the content will affect Perceived Usefulness and Playfulness, Goal Expectancy and Behavioral Intention as in the following hypotheses:
H12: Content will have a positive effect on Perceived Usefulness.
H13: Content will have a positive effect on Perceived Playfulness.
H14: Content will have a positive effect on Goal Expectancy.
H15: Content will have a positive effect on the Behavioral Intention to Use CBA.
(K. Weinerth et al., 2014) examined the usability when applying CBA [
The study involved 546 students from which 340 were females (62.3%) and 206 were males (37.7%). Most of the students’ age was between 17 and 23 years old. The students had a CBA exam that consisted of 45 multiple choice questions each of which has four possible answers. The questions displayed to students were randomly generated, and the assessment duration was 45 minutes after which every student had to answer a survey with 34 questions.
Construct | Measurement items |
---|---|
Perceived usefulness (PU) | PU1: Using the Computer Based Assessment (CBA) will improve my work. PU2: Using the Computer Based Assessment (CBA) will enhance my effectiveness. PU3: Using the Computer Based Assessment (CBA) will increase my productivity. |
Perceived ease of use (PE) | PE1: My interaction with the system is clear and understandable. PE2: It is easy for me to become skillful at using the system. PE3: I find the system easy to use. |
Computer self efficacy (CS) | CS1: I could complete a job or task using the computer. CS2: I could complete a job or task using the computer if someone showed how to do it first. CS3: I can navigate easily through the Web to find any information I need. CS4: I was fully able to use the computer and Internet before I began using the Computer Based Assessment (CBA). |
Social influence (SI) | SI1: People who influence my behavior think that I should use CBA. SI2: People who are important to me think that I should use CBA. SI3: The seniors in my university have been helpful in the use of CBA. SI4: In general, my university has supported the use of CBA. |
Facilitating conditions (FC) | FC1: When I need help to use the CBA, someone is there to help me. FC2: When I need help to learn to use the CBA, system’s help support is there to teach me. |
Content (CT) | CT1: CBA’s questions were clear and understandable. CT2: CBA’s questions were easy to answer. CT3: CBA’s questions were relative with the course’s syllabus. CT4: CBA’s questions were useful for my course. |
Goal expectancy (GY) | GY1: Courses’ preparation was sufficient for the CBA. GY2: My personal preparation for the CBA. GY3: My performance expectations for the CBA. |
Perceived playfulness (PP) | PP1: Using CBA keeps me happy for my task. PP2: Using CBA gives me enjoyment for my learning. PP3: Using CBA, my curiosity stimulates. PP4: Using CBA will lead to my exploration. |
Behavioral intention to use (BI) | BI1: I intend to use CBA in the future. BI2: I predict I would use CBA in the future. BI3: I plan to use CBA in the future. |
The current research uses a Structural Equation Modeling (SEM) approach based on AMOS 20.0 to study the causal relationships and to test the hypotheses between the observed and latent constructs in the proposed research model. SEM can be divided into two sub-models: a measurement model and a structural model. While the measurement model defines relationships between the observed and unobserved variables, the structural model identifies relationships among the unobserved/latent variables by specifying which latent variables directly or indirectly influence changes in other latent variables in the model [
The basis for data collection and analysis is a field study in which respondents answered all items on a five point Likert-scales ranging from 1 (strongly disagree) to 5 (strongly agree). Furthermore, elements used to consider each of the constructs were primarily obtained from prior research. These elements provided a valued source for data gathering and measurement as their reliability and validity have been verified through previous research and peer reviews. The model of Behavioral Intention (BI) to Use CBA constructs and their corresponding items (i.e. Perceived Usefulness (PU), Perceived Ease of Use (PE), Computer Self Efficacy (CS), Social Influence (SI), Facilitating Conditions (FC), Content (CT), Goal Expectancy (GY), Perceived Playfulness (PP) were adapted from [
Empirical data for this study was collected through paper-based survey in Jordan. Specifically, a survey questionnaire was used to gather data for hypotheses testing from at the University of Jordan. Before implementing the survey, the instrument was reviewed by three lecturers who are specialized in the Management Information Systems (MIS) discipline in order to identify problems with wording, content, and question ambiguity. After some changes were made based on their suggestions, the modified questionnaire was piloted on ten students who are studying at the university. Based on the feedback of this pilot study, minor edits were introduced to the survey questions, and the questionnaires were distributed to the participants. As per ethics policies, all potential participants were briefed about the nature of the work and were requested to provide explicit approval. The population of this study consists of all students who studied Introduction to Electronic Commerce Course as elective course during the first semester 2013-2014 from the University of Jordan located in Jordan, which counts of more than 570 according to the university’s registration unit. The sample size of this study was determined based on the rules of thumb for using SEM within AMOS 20.0 in order to obtain reliable and valid results. (R. Kline, 2010) suggested that a sample of 200 or larger is suitable for a complicated path model [
As showed in
All the 30 items were tested for their means, standard deviations, skewness, and kurtosis. The descriptive statistics presented below in
Category | Frequency | Percentage (%) |
---|---|---|
Gender | ||
Male | 206 | 37.7 |
Female | 340 | 62.3 |
Total | 546 | 100 |
Age | ||
17 years - less than 20 | 183 | 33.5 |
20 years - less than 23 | 315 | 57.7 |
23 years - less than 26 | 31 | 5.7 |
26 years - less than 30 | 9 | 1.6 |
30 years and above | 8 | 1.5 |
Total | 546 | 100 |
Academic level | ||
Year 1 | 57 | 10.4 |
Year 2 | 219 | 40.1 |
Year 3 | 172 | 31. 5 |
Year 4 | 72 | 13.2 |
Year 5 | 26 | 4.8 |
Total | 546 | 100 |
Number of daily hours using different types of information technology | ||
Less than half an hour | 14 | 2.6 |
Half an hour - 1 hour | 95 | 17.4 |
1 hour - less than 3 hours | 200 | 36.6 |
3 hours and above | 237 | 43.4 |
Total | 546 | 100 |
Construct/items | Mean | S.D | Order | Rank | Skewness | Kurtosis |
---|---|---|---|---|---|---|
Perceived usefulness PU1: PU2: PU3: | 3.6520 3.6227 3.4945 | 1.00081 1.01933 1.04792 | 1 2 3 | Medium Medium Medium | −0.792 −0.747 −0.470 | 0.451 0.188 −0.422 |
Perceived ease of use PE1: PE2: PE3: | 3.6630 3.9103 3.9139 | 1.16209 1.02501 1.04658 | 3 2 1 | Medium High High | −0.783 −1.020 −1.061 | −0.146 0.632 0.749 |
Computer self efficacy CS1: CS2: CS3: CS4: | 4.1190 4.1813 4.4377 4.3187 | 0.85592 0.83617 0.75222 0.80200 | 4 3 1 2 | High High High High | −1.217 −1.334 −1.669 −1.302 | 2.024 2.473 3.692 1.897 |
Social influence SI1: SI2: SI3: SI4: | 3.4780 3.5110 3.6099 3.9469 | 1.03004 1.04261 1.03153 0.87310 | 4 3 2 1 | Medium Medium Medium High | −0.512 −0.478 −0.705 −1.125 | −0.276 −0.362 0.099 1.1771 |
Facilitating conditions FC1: FC2: | 3.4123 3.4121 | 1.03895 1.04374 | 1 2 | Medium Medium | −0.454 −0.480 | −0.505 −0.538 |
Content CT1: CT2: CT3: CT4: | 3.4139 3.0971 3.3956 3.4945 | 1.16718 1.13320 1.03032 1.02490 | 3 2 1 4 | Medium Medium Medium Medium | −0.531 −0.131 −0.587 −0.693 | −0.545 −0.738 −0.115 0.069 |
---|---|---|---|---|---|---|
Goal expectancy GY1: GY2: GY3: | 2.8553 3.3498 3.1026 | 1.21275 1.08212 1.15755 | 3 1 2 | Medium Medium Medium | −0.024 −0.346 −0.229 | −1.028 −0.601 −0.684 |
Perceived playfulness PP1: PP2: PP3: PP4: | 3.3736 3.3938 3.4194 3.4377 | 1.16034 1.14165 1.12107 1.10244 | 4 3 2 1 | Medium Medium Medium Medium | −0.480 −0.502 −0.503 −0.072 | −0.535 −0.457 −0.349 −0.990 |
Behavioral intention to use BI1: BI2: BI3: | 3.9414 4.0513 3.9249 | 1.09051 1.01959 1.09538 | 2 1 3 | High High High | −1.110 −1.031 −0.910 | 0.852 0.686 0.148 |
Model | x2 | df | p | x2/df | IFI | TLI | CFI | RMSEA |
---|---|---|---|---|---|---|---|---|
Initial model | 970.242 | 369 | 0.000 | 2.629 | 0.93 | 0.91 | 0.93 | 0.055 |
Final model | 572.977 | 288 | 0.000 | 1.990 | 0.96 | 0.95 | 0.96 | 0.043 |
Confirmatory factor analysis (CFA) was conducted to check the properties of the instrument items. Indeed, prior to analyzing the structural model, a CFA based on AMOS 20.0 was conducted to first consider the measurement model fit and then assess the reliability, convergent validity and discriminant validity of the constructs [
Unidimensionality is the extent to which the study indicators deviate from their latent variable. An examination of the unidimensionality of the research constructs is essential and is an important prerequisite for establishing construct reliability and validity analysis [
Constructs and indicators | Std. loading | Std. error | Square multiple correlation | Error variance | Cronbach alpha | Composite reliability | AVE |
---|---|---|---|---|---|---|---|
Perceived usefulness | 0.866 | 0.86 | 0.68 | ||||
PU1 | 0.810 | *** | 0.656 | 0.344 | |||
PU2 | 0.866 | 0.051 | 0.749 | 0.260 | |||
PU3 | 0.808 | 0.052 | 0.653 | 0.380 | |||
Perceived ease of use | 0.846 | 0.82 | 0.61 | ||||
PE1 | 0.805 | *** | 0.648 | 0.474 | |||
PE2 | 0.802 | 0.044 | 0.644 | 0.374 | |||
PE3 | 0.812 | 0.045 | 0.659 | 0.373 | |||
Computer self efficacy | 0.777 | 0.84 | 0.58 | ||||
CS1 | 0.674 | *** | 0.455 | 0.399 | |||
CS2 | 0.567 | 0.074 | 0.322 | 0.473 | |||
CS3 | 0.780 | 0.072 | 0.608 | 0.221 | |||
CS4 | 0.738 | 0.075 | 0.544 | 0.293 | |||
Social influence | 0.746 | 0.78 | 0.54 | ||||
SI1 | 0.826 | *** | 0.682 | 0.336 | |||
SI2 | 0.830 | 0.053 | 0.688 | 0.338 | |||
SI3 | 0.566 | 0.053 | 0.320 | 0.722 | |||
Facilitating conditions | 0.772 | 0.76 | 0.61 | ||||
FC1 | 0.778 | *** | 0.606 | 0.425 | |||
FC2 | 0.808 | 0.118 | 0.653 | 0.378 | |||
Content | 0.884 | 0.82 | 0.53 | ||||
CT1 | 0.782 | *** | 0.612 | 0.528 | |||
CT2 | 0.750 | 0.052 | 0.562 | 0.561 | |||
CT3 | 0.749 | 0.047 | 0.561 | 0.465 | |||
CT4 | 0.756 | 0.047 | 0.571 | 0.450 | |||
Goal expectancy | 0.617 | 0.66 | 0.50 | ||||
GY2 | 0.502 | *** | 0.196 | 0.640 | |||
GY3 | 0.862 | 0.220 | 0.744 | 0.343 | |||
Perceived playfulness | 0.772 | 0.88 | 0.72 | ||||
PP1 | 0.900 | *** | 0.811 | 0.255 | |||
PP2 | 0.895 | 0.032 | 0.801 | 0.259 | |||
PP3 | 0.833 | 0.034 | 0.694 | 0.384 | |||
Behavioral intention to use | 0.854 | 0.84 | 0.64 | ||||
BI1 | 0.901 | *** | 0.811 | 0.224 | |||
BI2 | 0.762 | 0.042 | 0.581 | 0.435 | |||
BI3 | 0.781 | 0.045 | 0.609 | 0.468 |
Reliability analysis is related to the assessment of the degree of consistency between multiple measurements of a variable, and could be measured by Cronbach alpha coefficient and composite reliability [
As shown above, since the measurement model has a good fit; convergent validity and discriminant validity can now be assessed in order to evaluate if the psychometric properties of the measurement model are adequate.
Although reliability is considered as a necessary condition of the test of goodness of the measure used in research, it is not sufficient [
Furthermore, as convergent validity test is necessary in the measurement model to determine if the indicators in a scale load together on a single construct; discriminant validity test is another main one to verify if the items developed to measure different constructs are actually evaluating those constructs [
In order to examine the structural model it is essential to investigate the statistical significance of the standardized regression weights (i.e. t-value) of the research hypotheses (i.e. the path estimations) at 0.05 level (see
Constructs | PU | PE | CS | SI | FC | CT | GY | PP | BI |
---|---|---|---|---|---|---|---|---|---|
PU | 0.68 | ||||||||
PE | 0.55 | 0.61 | |||||||
CS | 0.29 | 0.51 | 0.58 | ||||||
SI | 0.59 | 0.58 | 0.35 | 0.54 | |||||
FC | 0.27 | 0.34 | 0.17 | 0.33 | 0.61 | ||||
CT | 0.54 | 0.66 | 0.34 | 0.47 | 0.40 | 0.53 | |||
GY | 0.45 | 0.64 | 0.27 | 0.46 | 0.36 | 0.52 | 0.50 | ||
PP | 0.53 | 0.70 | 0.34 | 0.42 | 0.35 | 0.54 | 0.47 | 0.72 | |
BI | 0.31 | 0.36 | 0.30 | 0.31 | 0.30 | 0.37 | 0.34 | 0.38 | 0.44 |
Note: Diagonal elements are the average variance extracted for each of the nine constructs. Off-diagonal elements are the squared correlations between constructs.
Research proposed paths | Coefficient value | t-value | p-value | Empirical evidence |
---|---|---|---|---|
H1: Perceived playfulness → behavioral intention to use | 0.104 | 1.975 | 0.049 | Supported |
H2: Perceived usefulness → behavioral intention to use | 0.008 | 0.164 | 0.870 | Not supported |
H3: Perceived usefulness → perceived playfulness | 0.156 | 4.290 | 0.000 | Supported |
H4: Perceived ease of use → behavioral intention to use | 0.024 | 0.522 | 0.602 | Not supported |
H5: Perceived ease of use → perceived usefulness | 0.175 | 5.131 | 0.000 | Supported |
H6: Perceived ease of use → perceived playfulness | 0.264 | 8.285 | 0.000 | Supported |
H7: Computer self efficacy → perceived ease of use | 0.618 | 11.012 | 0.000 | Supported |
H8: Social influence → perceived usefulness | 0.343 | 9.231 | 0.000 | Supported |
H9: Facilitating conditions → perceived ease of use | 0.209 | 5.570 | 0.000 | Supported |
H10: Goal expectancy → perceived usefulness | 0.118 | 2.490 | 0.000 | Supported |
H11: Goal expectancy → perceived playfulness | 0.376 | 9.797 | 0.000 | Supported |
H12: Content → perceived usefulness | 0.156 | 3.594 | 0.000 | Supported |
H13: Content → perceived playfulness | 0.283 | 7.050 | 0.000 | Supported |
H14: Content → goal expectancy | 0.605 | 16.859 | 0.000 | Supported |
H15: Content → behavioral intention to use | 0.044 | 0.833 | 0.405 | Not supported |
The coefficient of determination for Goal Expectancy, Perceived Usefulness, Perceived Playfulness, Perceived Ease of Use, and Behavioral Intention to Use were 0.34, 0.20, 0.47, 0.22, and 0.10 respectively, which indicates that the model does quite account for the variation of the proposed model.
Nowadays, students’ learning performance and outcome are evaluated using CBA rather than PBA. Our research purpose is to explore and identify the influential factors that affect the students’ attitude toward using CBA in higher education. Researchers are working in this research area to help institutions to have a successful implementation for CBA. In the literature, Perceived Usefulness, Perceived Ease of Use, Perceived Playfulness, and Perceived Importance considered as a main elements in Behavioral Intention to use CBA [
The study shows that Perceived Playfulness has direct impact on Behavioral Intention, while the constructs which have indirect impact on Behavioral Intention are Perceived Usefulness, Perceived Ease of Use, Content, Computer Self Efficacy, Facilitating Conditions, Social Influence and Goal Expectancy (see
Regarding Goal Expectancy, it was shown that students find a CBA useful and playful when they have good expectations from the system. Moreover, the positive effect of Social Influence on Perceived Usefulness provided by TAM2 was also supported by this model. Additionally, Perceived Ease of Use is positively impacted by Computer Self Efficacy and Facilitating Conditions as shown by the study. Furthermore, Perceived Ease of Use has a direct impact on Perceived Usefulness and Perceived Playfulness. While previous studies show that Perceived Usefulness and Perceived Ease of Use have a direct impact on Behavioral Intention, the study of this model shows that they have only an indirect impact through Perceived Playfulness.
Therefore, the results of this study confirm the results’ of prior study conducted by [
Dependent variables | R2 | Independent variables | Direct effects | Indirect effect | Total effect |
---|---|---|---|---|---|
Behavioral intention to use | 0.10 | Perceived playfulness | 0.104 | 0.000 | 0.104 |
Perceived usefulness | 0.008 | 0.017 | 0.025 | ||
Perceived ease of use | 0.024 | 0.032 | 0.056 | ||
Computer self efficacy | 0.000 | 0.034 | 0.034 | ||
Social influence | 0.000 | 0.008 | 0.008 | ||
Facilitating conditions | 0.000 | 0.012 | 0.012 | ||
Goal expectancy | 0.000 | 0.039 | 0.039 | ||
Content | 0.044 | 0.057 | 0.101 | ||
Perceived playfulness | 0.47 | Perceived usefulness | 0.156 | 0.000 | 0.156 |
Perceived ease of use | 0.263 | 0.028 | 0.291 | ||
Computer self efficacy | 0.000 | 0.179 | 0.179 | ||
Social influence | 0.000 | 0.054 | 0.054 | ||
Facilitating conditions | 0.000 | 0.061 | 0.061 | ||
Goal expectancy | 0.376 | 0.003 | 0.379 | ||
Content | 0.283 | 0.254 | 0.537 | ||
Perceived usefulness | 0.20 | Perceived ease of use | 0.175 | 0.000 | 0.175 |
Computer self efficacy | 0.000 | 0.108 | 0.108 | ||
Social influence | 0.343 | 0.000 | 0.343 | ||
Facilitating conditions | 0.000 | 0.037 | 0.037 | ||
Goal expectancy | 0.018 | 0.000 | 0.018 | ||
Content | 0.156 | 0.011 | 0.167 | ||
Perceived ease of use | 0.22 | Computer self efficacy | 0.618 | 0.000 | 0.618 |
Facilitating conditions | 0.209 | 0.000 | 0.209 | ||
Goal expectancy | 0.34 | Content | 0.605 | 0.000 | 0.605 |
Hypothesis | Path | Terzis et al. results | This research result |
---|---|---|---|
H1 | PP → BI | Supported | Supported |
H2 | PU → BI | Not supported | Not supported |
H3 | PU → PP | Supported | Supported |
H4 | PEOU → BI | Supported | Not supported |
H5 | PEOU → PU | Supported | Supported |
H6 | PEOU → PP | Supported | Supported |
H7 | CSE → PEOU | Supported | Supported |
H8 | SI → PU | Supported | Supported |
H9 | FC → PEOU | Supported | Supported |
H10 | GE → PU | Supported | Supported |
H11 | GE → PP | Supported | Supported |
H12 | C → PU | Supported | Supported |
H13 | C → PP | Supported | Supported |
H14 | C → GE | Supported | Supported |
H15 | C → BI | Not supported | Not supported |
This study investigated the factors that influenced the students’ behavior toward intention to use a computer based assessment in higher education. The tested model and measurement were supported from the collected data. Our research results demonstrate that Perceived Playfulness has a direct effect on Behavioral Intention to Use CBA, which aligns with [
The study shows that Perceived Playfulness has a direct effect on CBA use. Perceived Ease of Use, Perceived Usefulness, Computer Self Efficacy, Social Influence, Facilitating Conditions, Content and Goal Expectancy have only indirect effects. Consequently, educators and developers have to achieve the students’ playfulness through using CBA. The study concludes that a system is more likely to be used by students if it is playful and CBA is more likely to be playful when it is easy to use and useful. Finally, the studied acceptance model for computer based assessment explains approximately only 10% of the variance of Behavioral Intention to Use CBA. Therefore, researchers need to investigate other variables that affect the Behavioural Intention.
MahmoudMaqableh,Ra’ed Moh’d TaisirMasa’deh,Ashraf BanyMohammed, (2015) The Acceptance and Use of Computer Based Assessment in Higher Education. Journal of Software Engineering and Applications,08,557-574. doi: 10.4236/jsea.2015.810053