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R E S E A R C H A R T I C L E

Advanced Science Letters Vol. 4, 1–7, 2011

Computer-Aided Learning: Unguided versus Guided Instruction

Catherine Conradty and Franz X. Bogner^∗

University of Bayreuth, Centre of Math and Science Education (Z-MNU), NW-1, D-95447 Bayreuth, Germany

Computer-aided multimedia learning provides learners with opportunities to individualize their own learning according to their own learning pace and needs, while at the same time it may cause cognitive overload for low achievers. Within the context of a computer-aided multimedia Biology lesson, the current study examined the effects of guided and unguided instruction on cognitive achievement and intrinsic motivation. Gender was taken into account and prior knowledge as a potential indicator of intrinsic load. The results showed that guided instruction had an effect on students reporting less tension and better perceived competence in successfully managing the learning task, as well as maintaining boys’ interest in the learning task even though no better learn- ing occurred. Decrease rates of newly acquired knowledge were significantly lower with an unguided instruction compared to a guided learning, irrespective of students’ level of prior knowledge or gender.

Keywords:

1. INTRODUCTION

1.1. Learning with Multimedia-Aided Learning Environments—Benefits and Disadvantages Many topics in Biology lessons deal with complex systems in both, the spatial or temporal dimension, and for this reason they cannot easily be taught using only verbal explanations or two- dimensional illustrations. Some subjects are too small to observe (e.g., cellular processes), others are too fast or long-termed (e.g., chemical reaction or seasonal processes), too abstract (carbon cycle) or multi-faceted (dynamics of food webs). Multimedia aids, such as films or animated simulations, may be useful tools in such lessons. Nowadays, multimedia aid is often a computer- based integration of ‘text, graphics, animation, video, imaging, and spatial modelling.’⁴⁹ The internet is a typical example of such a multimedia module with non-linear and non-sequential arrangement and display information (the so-called ‘hypermedia’

or ‘hypertext’). A hypertext is understood as a multi-linear organ- isation of objects in a net-structure, generated by connections of the contents of knowledge (named ‘knots’) with logical links.¹⁰ A node may consist of text, graphics, photographic images, ani- mations, sound, and audio or video-sequences. Instead of reading about the theory of some subject, students can actually see a process through animated simulations. Abstract theories become meaningful for students because they may visualize a normally not observable process.

The introduction of computer-aided learning environments is generally thought to lead to a learning revolution¹⁷offering new

∗Author to whom correspondence should be addressed.

ways of learning, new instructional approaches and even new competences. Hypermedia learning provides an opportunity of multimedial and multicodal information to appeal to learners of various learning styles.¹⁵³⁴ The dynamic of hypertexts provides an opportunity of self-regulated learning according the individual interests of the learner. For example, children at Indian villages learned computer handling and required skills (e.g., language or ability of self-regulation) just by playing with a computer.²³ Thus, hypertext can be a constructivist learning environment pro- viding a suitable learning setting deliberately supporting students in constructing knowledge.

The computer is considered to be an innovative ‘new medium’

for classroom learning It is deemed to substantially increase, for instance, motivation scores or provide additional cognitive learn- ing effects. Students seem to rate a computer as a new motivating tool and that is why computers are considered to promote cogni- tive learning.¹⁸

However, several problems could interfere with students’

learning success. A ‘toy’ computer may simply lead a trivial- isation of the subject matter:³⁷ Students consider conventional print media as learning tool as difficult to handle. This generally demands close attention. By contrast, audio and video approaches are more likely to be considered as media for relaxation instead of school work. This may reduce attention and consequently reduce cognitive learning achievements. Furthermore, computer- aided learning success may be suppressed by gender-specific characteristics (e.g., Ref. [32]) or by insufficient experience with computer causing cognitive load problems.⁴⁴ Since the begin- nings of hypertexts, the threat to get “lost in hyperspace,”¹⁰ is well discussed, but became even more complex with the web 2.0.

Adv. Sci. Lett. Vol. 4, No. xx, 2011 1936-6612/2011/4/001/007 doi:10.1166/asl.2011.2040 1

R E S E A R C H A R T I C L E

Computer-aided learning environments are differently dis- cussed: For some it is the evil (‘everything one tries to achieve educationally, one achieves better without computers’²¹, for others the solution (‘computers make children smarter’⁵. Of course, each medium needs an appropriate instructional design for an effective learning. Several aspects, such as complexities of computer-aided learning environments may interfere with an individual learning success and maybe overstrain a student’s cog- nitive and concentration capacity.⁹¹⁹²⁵

Students may not find relevant information in a hypermedia environment because of its non-linear structure:² For that rea- son, a well-structured hypertext design may help to prevent a

‘lost in space’ situation.¹⁰However, the linearity of conventional approaches very likely supports a linear thinking that may hide complex relationships and prevent a cross-linked thinking. An important step towards a good instructional design, therefore, may provide the understanding of the principles of multimedia design,²⁹e.g., focussing on the phenomenon of attention split²⁹³⁹ which might substantially contribute to cognitive load.⁴⁵ Addi- tionally, the modality of information seems to affect the limited working memory capacity and consequently learning success.³³³ Multimedia learning is a promising instructional method in supporting a deeper comprehension of complex and dynamic concepts, of which natural science lessons usually are full of.

Multimedia, especially in the form of computer-aided instruc- tion, gives an opportunity to attract students with diverse learning styles with its combination of written or spoken text or with graphics that with the help of movies or simulations can even illustrate the fourth dimension of time. This is especially help- ful for an understanding of dynamic processes which especially within the field of ecology exists. Furthermore, the interactiv- ity of computer-aided learning software offers the advantages of hands-on learning environments. To sum up, diverse causes may prevent learning within a multimedia learning environment, which partially may explain the heterogeneity of study outcomes in the field, not even to mention the variety of factors involved, e.g., diverse media, manuals, learners (e.g., age group).⁸

1.2. Effect of Teachers’ Guidance Style: Tutoring versus Individualized Learning

Multimedia learning systems are often used as ‘stand alone manuals’ for individualized knowledge acquisition. This might enhance autonomy, a feeling of competence and interest levels as students work self-directed and autonomously, often in small groups (comparable to workstation environments.⁴ A teacher’s role needs to shift from an ‘instructor’ to a ‘tutor’ of a learn- ing process.³⁸ According to the self-determination theory (e.g., Refs. [12, 20, 48]), any motivational enhancement is depend- ing on three psychological needs: autonomous support, perceived competence and social relatedness. The order of presentation of a subject matter and the engagement’s duration specifically coping a learner’s interest may provide even additional support for those Refs. [22, 38]. Within this context, a computer-aided lesson is an open learning environment with material-centred open learning approach generally regarded as highly student-oriented.⁴ Thus, computer-aided learning very likely may enhance motivation and in consequence learning success.

Controversial discussions about the impact of instructional guidance have been ongoing for about half a century (e.g., Refs. [1, 24, 30]). Any individual learning and learning with

hypertext in general, may demand a high amount of self- regulation competence from a learner.⁴⁰ It needs again pointed out that any competence acquisition is an often discussed benefit of multimedia learning environment. When this competence is not evolved sufficiently, a learning process may fail.

In our hypotheses any guidance during an individualized learn- ing will promote interest and lead to better learning achieve- ment levels compared to an unguided instruction (hypothesis I), and interest may strongly correlate with learning success (hypothesis II).

1.3. Effect of Prior Knowledge

Both, complex contents and instructional designs, demand a learner’s cognitive capacity:⁴⁴ described in their theory of cog- nitive load the effect of a limited working memory capacity on the process of information by assuming three cognitive load sub- components:

(i) an intrinsic load caused by content complexity,

(ii) extraneous load caused by instructional mode, e.g., computer-aided multimedia learning, and

(iii) germane load necessary for individually processing infor- mation towards long-term memory.

As all three components are presumed to be additive,⁴³ an increase in one component (i) and/or (ii) without a decrease in an other may individually cause cognitive overload. Available capacities for (iii) germane load would be reduced and a balance of all components reached consequently leading to a succesive learning result.

Content complexity is a subjective factor, dependent on a learner’s prior knowledge. The more a learner already knows about a specific subject, the easier is any adding of new knowledge.¹Applied to the cognitive load theory,

(i) intrinsic load is dependent on the learners’ individual prior knowledge and

(ii) computer-aided lessons interact with an individual extrane- ous load. Learners with prior knowledge may easier construct stable mental models embedded in existent knowledge.²⁸ Consequently, learners with high prior knowledge are expected to already own more long-term knowledge. Therefore, the third hypothesis of our current study focused on high prior knowledge students who are supposed to learn better than students with low prior knowledge. However, students with low prior knowledge may better benefit from guided instruction.

1.4. Gender

Studies of gender effects in hypermedia instruction have provided inconsistent results, by reporting no differences at all⁴⁶until sub- stantial differences with regard to interest in computers.³⁵ This may simply reflect an age effect: For instance, children moni- tored within the project ‘Hole-in-the-Wall Education’²³learned to handle computer together with additional skills, e.g., language.

Adolescents and adults seem to have a natural timidity towards learning by ‘try and error’ with an unknown technique.³¹ Pre- vious studies have shown that educational software motivates girls less than boys,³² a result that might be explained by the software being designed by males. In the 1990ies, studies in Germany described boys as more experienced with computers than girls¹⁴²⁶by concluding a higher competence for boys. Con- sequently, this outcome may contribute to our fourth hypothesis

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Adv. Sci. Lett. 4, 1–7, 2011

describing girls as less motivated and thus as less affected by computer-aided learning. Consequently, girls may learn less than boys in the context of computer-aided instruction.

1.5. Research Hypotheses

On the basis of the literature that has been reviewed, in this study we hypothesized that guidance during individualized learning will promote interest and lead to higher achievement compared to unguided instruction (hypothesis I), and that interest will cor- relate strongly with learning success (hypothesis II). A hypothe- sis III is that students with high prior knowledge will learn more than students with low pre-knowledge. However, students with low prior knowledge may better benefit from guided instruction.

Lastly, in our hypothesis IV, girls will be less motivated to learn with computer-aided instruction, and that computer-aided instruc- tion will negatively affect their learning.

2. METHODOLOGY

In this study, we assumed a quasi-experimental research design. Specifically, we used a before-after-control-impact design (BACI).⁴¹We measured cognitive knowledge before and after the treatment. To evaluate whether the effect was due to the treat- ment we included a treatment group with impact, and control groups with antithetic impact and with no impact respectively.

Teaching strategy was the independent variable. Specifically, (1) Computer-aided individualized learning with guidance (Guided) (n=126);

(2) Computer-aided individualized learning without guidance (Unguided) (n=102); and

(3) a control group (n=57).

The computer-aided lesson unit consisted of two ‘aquatic ecol- ogy’ units consistent with the Bavarian syllabus for the selected age group. This module highlighted the following topics with particular attention to environmental protection contexts: the development of a polliwog to a frog with physical adaptation to different environments; food webs, and seasonal nutrition cir- cle in the ecosystem lake. Both, the guided and the unguided treatment-groups were engaged in open learning whereas the unguided treatment group had free choice in using the learning software, and the guided followed a specific instruction within the software with no possibility to choose or to switch.

2.1. Participants and Procedures

We selected novice high-achieving 6th graders (n=228) (highest stratification level; ‘Gymnasium’) from 11 classes to participate in the study. This cohort already had sufficient ICT-experience because informatics was part of their regular syllabus. The par- ticipants’ mean age was 12.6 (SD=008) years. The gender distribution was perfectly balanced (50% to 50%). Our quasi- experimental BACI design⁴¹ included a control group (n=57) which received no instruction but completed all tests. A sin- gle teacher, unknown to all students, taught all lessons. After a 15 minute introduction phase, all students worked cooperatively in groups of two for about 45 minutes per topic. The group find- ing process was free of choice.

For the Guidedinstruction, each topic was introduced sepa- rately before students worked on their own in dyads. All units were taught subsequently. In practice, the individual working speed differed substantially. For this reason some students had

to wait and others were in a hurry. For theUnguided instruc- tion, an introduction about handling the computer was provided at the beginning of the lesson. After that, students started the computer-aided learning program on their own by switching units whenever desired. Teacher restricted their support to assist with technical problems. A workbook provided a guideline through the autonomous lesson. Every student completed the workbook autonomously.

2.2. Data Analysis 2.2.1. Knowledge Test

Prior knowledge and changes in the knowledge level were mea- sured by means of a pre-test (K1) applied two weeks before lesson participation, a post-test (K2) immediately after the lesson and a retention-test (K3) after 6 weeks (Table I). The question- naire covered the content of the lesson’s objectives. Altogether 17 items in multiple-choice format with four possible response options one of which was the correct were administered (see example Table II). Thus, the probability of yielding a correct response by pure guessing was 0.25.

To guarantee content validity, we constructed the items accord- ing to the learning objectives of the lessons.³⁶ Item difficul- ties, defined as percentage of correct answers⁶ needed to range between 0.2 and 0.8, otherwise they were discarded. The Cor- rected Item-Total Correlation needed a score between+0.3 and +0.5⁶ ours were in the +02− +05 range. Generally, knowl- edge tests are difficult to test for reliability although reliability coefficients less than 0.6 are used for differentiating groups.²⁷

Students had low pre-knowledge with 49.4% correct answers in the K1 (sum scoreM=8397 of 17) with a Cronbach’sof 0.60. The latter were higher within the post-test (K2 Cronbach’s of 0.75) as well as the retention-test (K3 Cronbach’sof 0.74).

Due to a non-normal distribution (Shapiro-Wilkp <00001), we applied non-parametric tests. Knowledge items were scored as correct (1 point) or incorrect (0 point) and further analysed as sum scores. Significance of learning success was calculated by using the Mann-Whitney-U-Test (MWU).

For a better understanding of the actual learning achievement observed, we calculated the differences K2-K1 (newly acquired knowledge), K3-K1 (consistently added knowledge) and K3-K2 (forgetting/drop rate).

• K2-K1 is the difference of post-test and pre-test. As the post test was immediately after the intervention, a positive result indi- cates increase in newly acquired knowledge.

• K3-K1 is the difference of retention-test and pre-test. As the retention-test is six weeks after the intervention, a positive result indicates increase in consistent knowledge.

Table I. Quasi experimental design^aof the study.

2 weeks before Pre-test K1

Computer aided Treatment prior

Intervention instruction knowledge Guided Unguided Control

Low 61 39 31

High 65 63 26

Ntotal 126 102 57

After the lesson Post-test K2 6 weeks after Retention-test K3

aThis design implied a one-way multivariate analysis of variance with repeated measures which had to be rejected on the grounds of the non-normal distribution of variables.

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R E S E A R C H A R T I C L E

Table II. Example for multiple choice questionnaires.

Question Multiple choice distractors

How is the larva’s transformation into Puberty

the mature animal named? Maturing

Metamorphosis Metabolism

• K3-K2 is the difference of the post-test and the retention-test after six weeks. Normally it is negative, because in the learning process parts of newly acquired knowledge are to be forgotten.

Thus, we call this difference drop rate or forgetting.

To estimate the hypothesized intrinsic load, we split the stu- dent sample into two subgroups: Pre low (with low prior knowledge) and Pre high (with high prior knowledge). The limit was the median of the gain in knowledge in K1 (8.5 points of 17). To analyse the correlations of cognitive knowl- edge tests with intrinsic motivation we computed Spearmans’

Rho.

2.2.2. Intrinsic Motivation Inventory (IMI)

To evaluate the program’s effects on a student’s intrinsic moti- vation, we applied four subscales of the ‘Intrinsic Motivation Inventory’ (IMI):¹²Interest/Enjoyment (IMI interest; 7 items;

e.g., ‘I enjoyed doing this activity very much.’), Perceived Com- petence (IMI competence; 6 items; e.g., ‘I think I am pretty good at this activity.’), Effort/Importance (IMI effort, 5 items;

e.g., ‘I put a lot of effort into this.’) and Pressure/Tension (IMI tension, 5 items; e.g., ‘I was very relaxed in doing this.’). The translated German version was taken from Ref. [38]). Students scored their rating on a Likert 5-point scale ranging from 1 (not true at all) to 5 (very true). We calculated average values between 1 and 5 for each subscale. The IMI in combination with the knowledge test was part of the post-test (K2). It was filled in immediately after the intervention (Table I).

3. RESULTS

3.1. Cognitive Achievement

There were no differences in prior knowledge between the treat- ment groups (Kruskal-Wallis Test²=5709, df=2,p=0058).

All students learned in both treatment groups but not in the con- trol (MWU K2: Z= −10,035, p <00001; K3: Z= −5,748, p <00001).

Students learning gains were obtained for both treatment groups. There is no significant difference between them in the post-test (K2), except in the retention-test (K3) (MWU K3Z=

−2,402,p=0016).

There was neither treatment nor gender effect in the differ- ences K2-K1 (newly acquired knowledge) and K3-K1 (consistent knowledge). In the drop rate K3-K2 was treatment effect with a smaller decrease in knowledge in the unguided approach (MWU Z= −2,236,p=0025; Fig. 1).

The high scoring prior knowledge group (Pre high)learned

‘less’ as they already had a level of knowledge. Thus, just the effect of the treatments on the low and high prior knowledge scores was regarded as pedagogical relevant (see discussion).

Low prior knowledge scorer (Pre low) forgot less in treatment Unguided (MWU Z= −2,561, p=0010; Fig. 2). Students

Fig. 1. Differences of knowledge tests (new, consistent knowledge and for- getting) with grouping variable treatment.

with high prior knowledge (Pre high)were not affected by the treatments.

No gender effects at all were found, neither in the specific treatment groups nor in the total sample without treatments.

3.2. Intrinsic Motivation Inventory (IMI)

For the intrinsic motivation inventory we calculated average val- ues between 1 and 5 for each subscale. For the subscales IMI competence (mean=35),IMI effort (mean=34) and IMI tension(mean=19), no significant differences with regard to treatments or gender were found. Just for the IMI interest score, a significant difference between boys and girls was found in the case of theUnguided intervention (MWUZ= −2,764, p=0006; Fig. 3). The only significant effect of treatment we found was on boys’ interest (MWU Z= −2,743, p=0006;

Fig. 3). Girls did not seem to be effected by the treatment in any subscales.

Fig. 2. differences of knowledge tests (new, consistent knowledge and for- getting) with grouping variable treatment and prior knowledge (^∗∗∗p <0001;

∗∗p=0003;^∗p=001).

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