Home PublicationsJRTEPast IssuesVolume 33 Number 5

Edited by Dr. David J. Ayersman, Mary Washington College, and Dr. W. Michael Reed, New York University

Volume 33 Number 5 Summer 2001

Controlling the Display of Animation for Better Understanding, Part II

Shu-Ling Lai
Ling Tung College

Results

Means and standard deviations for individual posttest performance, time on CBL task, attitude toward CBL, and attitude toward controlling are reported in Table 1. The pretest revealed that subjects had little prior knowledge of the program content (M = 8% correct). The pretest was not used as the covariate because no significant difference was found among three treatment groups, F(2,183) = 0.11, P = 0.89.

Table 1. Means and Standard Deviations for All Dependent Variables
Treatment Group	Program Control				Linear Control				Learner Control				Total
Dependent Variable	M		SD		M		SD		M		SD		M		SD
Posttest
Low (n = 92)	12.56		3.12		11.31		2.84		10.20		2.92		11.46		3.05
High (n = 94)	13.11		2.91		11.03		2.76		12.72		2.58		12.31		2.88
Total (N = 186)	12.85		3.00		11.17		2.79		11.60		2.95
CBL Time
Low (n = 92)	37.44		5.91		35.35		5.61		36.81		5.75		36.54		5.77
High (n = 94)	37.23		4.49		33.63		6.04		33.50		5.29		34.86		5.52
Total (N = 186)	37.33		5.18		34.47		5.85		35.13		5.72
Attitude–CBL
Low (n = 92)	3.63		0.47		3.74		0.57		3.54		0.44		3.65		0.51
High (n = 94)	3.63		0.61		3.56		0.49		3.82		0.50		3.67		0.54
Total (N = 186)	3.63		0.55		3.66		0.53		3.68		0.49
Attitude–Control
Low (n = 92)	3.59		0.33		3.69		0.50		3.59		0.45		3.62		0.43
High (n = 94)	3.47		0.61		3.52		0.35		3.77		0.37		3.57		0.42
Total (N = 186)	3.53		0.42		3.60		0.43		3.68		0.42

The results of the ANOVA followed by posthoc comparisons on all dependent variables are summarized in Table 2. For the dependent variable of the posttest, subjects assigned to the program control group performed significantly better than those in the linear or learner control group F(2,1830 = 6.00, P = 0.003. ANOVA results also revealed that subjects with higher mathematical ability performed better than subjects with lower mathematical ability, F(1,184) = 4.09, P = 0.044 on the programming concept learning. A significant interaction effect, F(2,183) = 3.17, P = 0.044 was found between treatment and ability (Figure 2) on the achievement posttest. Subjects with lower ability performed worse when given learner control. On the other hand, subjects with higher ability performed worse when given a linear control. Both lower- and higher-ability students performed better with program control.

Table 2. Analysis of Variance Table for All Dependent Variables
Dependent Variable/Source	SS		df		MS		F		p
Posttest
Treatment	98.19		2		49.09		6.00		0.003
Ability	33.53		1		33.53		4.09		0.044
Treatment x ability	51.91		2		25.96		3.17		0.044
CBL time
Treatment	289.36		2		144.68		4.75		0.010
Ability	147.25		1		147.25		4.83		0.029
Treatment x ability	77.80		2		38.90		1.28		0.281
CBL attitude
Treatment	0.08		2		0.04		0.14		0.866
Ability	0.04		1		0.04		0.14		0.709
Treatment x ability	1.78		2		0.89		3.28		0.040
Attitude—control
Treatment	0.69		2		0.35		1.97		0.142
Ability	0.06		1		0.06		0.36		0.550
Treatment x ability	1.01		2		0.50		2.86		0.050

Figure 2. The interaction effect between treatment and ability level on posttest.

For the dependent variable of CBL time, students in the program control group took a significantly longer time than students in the linear or learner control groups F(2,183) = 4.75, P = 0.01. Lower-ability students took a significantly longer time to complete the CBL than higher-ability students F(1,184) = 4.83, P = 0.03. No significant difference was found for the interaction effect F(2,183) = 1.28, P = 0.281.

The result showed that students generally enjoyed working with a CBL program (M = 3.66, SD = 0.52). Results of ANOVA on attitudes toward CBL and controlling revealed no significant difference for the main effect of treatments and ability levels. However, the interaction effects between treatment and ability on attitudes toward CBL, F(2,183) = 3.28, P = 0.04 and controlling, F(2,183) = 2.86, P = 0.05 all achieved significant levels. As shown in Figure 3, lower-ability students preferred linear control. A totally opposite result was obtained for higher-ability students. Subjects with higher-ability preferred learner control to linear control. A similar result was obtained for the attitudes toward controlling (Figure 4). Lower-ability students preferred linear control and higher-ability students preferred learner control.

Figure 3. The interaction effect between the treatment and ability on attitude toward CBL.

Figure 4. The interaction effect between treatment and ability on attitude toward controlling.

Discussion and Conclusions

Animation and Mental Model

The current research results run counter to the common belief that students who control their course of study perform better. These results support previous research that, if a learner is a novice and if a given task requires more effort, program control is suggested (Chung & Reigeluth, 1992; Clark & Taylor, 1994). Animation offers a potentially powerful medium that helps learners build mental representations and comprehension (Mayer & Gallini, 1990; Shih & Alessi, 1994) that requires more cognitive effort than static graphics (Rieber, 1990, 1995; Schnotz & Grzondziel, 1996). The continuous sequence of animation in the program control provides learners with a systematic and completed conceptual model that supports mental simulation and helps them assimilate new concepts (Lai, in press; Schnotz & Grzondziel). In this study, students with both higher and lower ability in mathematics performed better with program control than linear and learner control. These results suggest that the presence of continuous animation in the program control may act as a systematic mental model, providing a fertile ground in which learners can incorporate new material into their cognitive structures. In the linear and learner control presentations, chunking the animation into segments might reduce cognitive overload (Clark & Taylor, 1994). However, linear and learner control require learners to control the sequence. The controlling process might direct learners’ attention (Chung & Reigeluth, 1992) and curtail the effectiveness and efficiency of assimilated learning (Spotts & Dwyer, 1996). Therefore, when animation is used to provide a conceptual model, program control is suggested. Program control can deepen students’ attention on the relevant information and at the same time can help learners build connections between abstract and concrete domains.

Engagement and Time on Task

Superior performance in the program control can also be explained by the fact that students took more time on the CBL task than the other two groups. The program control features a predetermined path that forces students to complete the task and study the whole package of instruction with more engagement (Cho, 1995). Direct observation of student interaction in this research revealed that students in the learner control group omitted many executions of animation. According to Block’s mastery learning theory (1971), under appropriate instructional conditions virtually all, rather than some, students can learn most of what they are taught. Additionally, animation has richly detailed visuals that require learners to search for essential learning cues. Therefore, our research suggests that increasing the time that students spend on CBL tasks with animation through the design strategy of program control would lead to more chances of engagement and, hence, has a better chance to enhance their understanding.

Learner Control and Mathematical Ability

Students with higher ability in mathematics performed significantly better in this study than did lower-ability students. These results support previous research that mathematical ability influences performance in learning programming (Lai & Repman, 1996). Bayman and Mayer (1988) explain that students with high mathematical ability tend to use models they have already developed to interpret learning and that a new mental model may actually distract their learning. However, students with low mathematical ability who presumably lack self-developed models would benefit from a relevant conceptual model provided in the instruction. In this study, program control provides learners a systematic mental model. Weaker students are more likely to benefit from program control than students with strong quantitative backgrounds who possess their own mental model. Therefore, a program control version is suggested for students with low mathematical ability who have less prior knowledge and lack self-developed mental models. Learner control would be more likely to meet the needs of students with higher math abilities.

As expected, when given more control, lower-ability students were significantly less efficient (Gay, 1986) in their use of time. Students assigned to the lower-ability group took a significantly longer time to complete the CBL lesson and performed significantly worse on the posttest than students assigned to the higher-ability group. One reason for this might be that lower-ability students are more likely to read the information presented on the screen slower than higher-ability students (Sherman & Klein, 1995). The present results are similar to previous research (Carpenter & Just, 1992), which found that it is probably more difficult for students with low mathematical knowledge to construct a visual representation of the abstract concept. Students who have higher ability and better background may find it easier to construct or reconstruct schemes in ways that are meaningful to them (Chung & Reigeluth, 1992).

The difference in the amount of time spent on task between students in different ability groupings is one indication why lower-ability students cannot afford extra effort in learner control. Previous research (Carrier, 1984; Freitag & Sullivan, 1995; Mager, 1964; Merrill, 1980) suggests that individual learners should best know their own needs and are uniquely qualified to act on that knowledge. However, for a learner with less prior knowledge, more interactions or more control might cause cognitive overload (Park, 1992; Stoney & Wild, 1998; Tsai, 1989). Students often make poor instructional choices when they are faced with complex instructional content or when they do not have sufficient prior knowledge (Carrier; Gay, 1986). Therefore, a straightforward teaching process with no learner control is suggested for teaching students with lower abilities.

Learner Control and Attitude Measure

The above-average mean on the attitude measure either toward the CBL lesson or toward control suggested that students generally held favorable attitudes toward the instructional program and controlling elements of animation. There were attitude differences attributed to the interaction effect of treatments and ability groupings. Students in the higher-ability group expressed positive attitudes under learner control and performed better. Students in the lower-ability group expressed negative attitudes under learner control and performed worse. It is interesting to note that higher-ability students’ performance is consistent with and related to their attitudes. Results support previous research that higher-ability students prefer more control and know their needs best (Morrison, 1992). The linear control forced all learners to go through the whole sequence by pressing the buttons one by one and limited the amount of control available to the learners. On the other hand, the line-by-line illustration seemed to slow down the speed of presentation and required less decision making. These limitations might give lower-ability students more confidence to learn. Therefore, lower-ability students prefer linear control most, although chunking the animation might interfere with their systematic learning of mental models as measured by the lower posttest performance.

Implications and Future Research

The study results provide implications for the design of computer-based learning. For abstract concept learning, it is suggested that designers can provide a more complete and thoughtful display with less user involvement. Moreover, the study also suggests that accommodating learners’ individual differences to the design of CBL lesson is an important concern (see also Belland et al,. 1985). It is incorrect, however, to assume that learner control is the best form of microcomputer instruction for all learners. Future research should include an investigation in to the best delayed time and elapsed time for displaying animation to systematically incorporate animation into the teaching process.

Contributor

Shu-Ling Lai is a professor in the Department of Digital Communication and Design and dean of the Design College at Ling Tung College in Taiwan, whose major research fields include computer-based learning, instructional design, and multimedia.

Contact

Shu-Ling Lai
Ling Tung College
1 Ling Tung Rd.
Nantun, Taichung 408
Taiwan, ROC
sllai@mail.ltc.edu.tw

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