Overall Satisfaction and General Perceptions

To understand the overall satisfaction of the learners after interacting with our system, six statements were provided, which are:

 Q15: I like using the interface of this system.  Q16: It was simple to use this system.

 Q17: I feel comfortable using this system.

 Q18: Overall, I was very satisfied with the presentation of instructional material.

 Q19: Overall, I was very satisfied with the system.  Q20: Overall, I had a very positive learning experience.

From Figure 5-14 we see that the majority results of participants were above neutral. The means illustrated in Figure 5-15 show that they were satisfied using the system, since the results were above 3 (neutral) of the scale of 5 (from strongly disagree to strongly agree). However, in Q15, FFIE learners have the lowest mean value in Figure 5-15, because four participants chose “Neutral”, two participants chose “Agree” and another two participants chose “Strongly Agree”. To find out the explanation for this, we found from the open statements of the questionnaire that those four participants who chose “Neutral” had the following perception about the WBI program’s interface in the questionnaire’s open questions:

 “The interface was very simple.”

 “Not much in design and color scheme.”  “Needs search instead of index.”

However, these results have no effect in changing the design of our WBI, which was based on the three system features.

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Figure 5-15: Means plot diagrams for Q15 to Q20 for each Multi-ID

It is clear that successful web applications rely upon the capability of the application to meet the needs and preferences of each learner. Thus, if a learner’s preferences are successfully met, they will have a more beneficial interaction with the WBI program and complete their tasks in a more efficient and effective way, and be satisfied in using such well-designed systems.

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5.6. Summary

In this chapter, we investigated learners’ preferences and perception using our WBI program designed to accommodate their needs using three system features: navigation tools, content scope and display options.

Firstly, we analyzed the preferences of each one of the individual differences using navigation tools and compared our findings with previous studies. We then analyzed several combinations of intersected individual differences to investigate how each combination influenced the learning preferences based on our individual tests. We noted that some individual differences and their intersection had an impact on learners' preferences when choosing navigation tools. We found that the related individual differences therefore altered a learner’s preferences, and that the designers of WBI applications need to consider the combination of individual differences rather than considering them individually. Furthermore, it was clear that learning preferences are influenced by gender when combined with prior knowledge. It may not be necessary, though, for the designers of hypermedia systems to consider prior knowledge as a part of the design process, since our results appear to show that prior knowledge did not influence the navigational preferences of participants individually. However, the combination of individual differences needs to be considered. Moreover, our findings demonstrate that such multiple individual differences (Multi-ID) have an impact on learners’ preferences and perception of using WBI systems. Finally, it is nice to note that they were satisfied using our WBI program.

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CHAPTER SIX

INVESTIGATING ATTRIBUTES

AFFECTING THE

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6.1. Introduction

Numerous research studies have explored the effect of hypermedia on learners’ performance using web-based instruction (WBI). In this chapter, we investigate how differences between individuals influenced learners’ performance using a hypermedia system to accommodate an individual’s preferences.

The literature on the effects of hypermedia systems on user performance (as discussed in Chapter Two) focuses extensively on measurement attributes such as time spent using the system by a user, gain score (g-score) and number of pages visited in the system. In this chapter, we use a data mining approach to analyze the results by comparing between two clustering algorithms (K-means and hierarchical) using two different numbers of clusters in each comparison. As shown previously in Chapter Four, individual differences had a significant impact on learner behaviour in our WBI program. Additionally, we found that the attributes that measure performance played an influential role in exploring a learner’s performance. In this chapter, the relationship between such measuring attributes induced rules in measuring levels of learners’ performance. Additionally, in Chapter Five, we analyzed several combinations of individual differences (cognitive style, prior knowledge, and gender) to investigate how each combination influences the learning preferences. We found that the related individual differences therefore altered a learner’s preferences. Therefore, WBI applications need to consider the combination of individual differences rather than considering them individually. Thus, in this chapter, we consider the combination of individual differences (Multi-ID) when exploring level of learners’ performance instead of considering their performance individually. In particular, we attempt to answer the following research questions:

RQ5: “What are the relationships between attribute values in measuring the performance level of the individual differences?”

RQ6: “How does the behaviour of individual differences influence a learner’s performance using three performance measurement attributes?”

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In an attempt to answer RQ5, we understand the relationships between measurement attributes (gain scores, number of pages visited in a WBI program and time spent on such pages) to explore the performance level.

In an attempt to answer RQ6, we investigate the influence of individual differences on learning performance level by exploring relationships between measurement attributes that affect performance level.

The chapter is structured as follows. In Section 6.2, we present related work about attributes used in measuring the learners’ performance and techniques applied to the analysis of the corresponding data. Findings of our analyses when comparing the results of two clustering algorithms, k-means and hierarchical clustering algorithms using two different numbers of clusters (4 and 5 clusters) are discussed in Section 6.3. Section 6.4 presents the discussions and conclusion of our study. Additionally, this section suggests rules of the performance levels and provides the best and the worst performance levels of learners with Multi-ID. At the end of this chapter, a summary is presented in Section 6.5.