Tool use in computer-based learning environments: towards a research framework

omputers in
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Computers in Human Behavior 22 (2006) 389–411

www.elsevier.com/locate/comphumbeh

Human Behavior

Tool use in computer-based learningenvironments: towards a research framework

Geraldine Clarebout *, Jan Elen

Center for Instructional Psychology and Technology, University of Leuven, Vesaliusstraat 2,

B-3000 Leuven, Belgium

Available online 11 November 2004

Abstract

Computer-based learning environments often confront learners with a number of tools,

i.e. non-embedded support devices. Such environments assume learners to be good judges

of their own learning needs. However, research indicates that students do not always make

adequate choices for their learning process. This especially becomes an issue with the use

of open learning environments, which are assumed to foster the acquisition of complex

problem solving skills. Such open learning environments offer students tools to support

their learning. Consequently, it is needed to understand factors that influence tool use

and acquire insight in learning effects of tool use. Both issues are addressed in this contri-

bution. A review of the existing literature has been undertaken by performing a search on

the Web of Science and the PsycInfo database. Results indicate that there is some evidence

for learner, tool and task characteristics to influence tool use. No clear indication was

found for a learning effect of tool use. The conclusion proposes a research framework

for the systematic study of tools.

� 2004 Elsevier Ltd. All rights reserved.

Keywords: Computer-based learning environments; Tool use; Literature review

0747-5632/$ - see front matter � 2004 Elsevier Ltd. All rights reserved.

doi:10.1016/j.chb.2004.09.007

* Corresponding author. Tel.: +32 16 32 5745; fax: +32 16 32 6274.

E-mail addresses: [email protected] (G. Clarebout), [email protected].

ac.be (J. Elen).

mailto:[email protected]

mailto:[email protected].

390 G. Clarebout, J. Elen / Computers in Human Behavior 22 (2006) 389–411

Computer-based learning environments regularly provide learners with a variety

of support devices to foster learning. These support devices can be either embedded

or non-embedded. Embedded support devices are totally integrated in the learning

environment and cannot but be considered by learners. Examples of such devices

are feedback, or the information structure in learning materials. Embedded supportdevices are devices with which learners are confronted without them having to re-

quest or ask for them. By contrast, non-embedded support devices are support

devices whose use depends on the learner�s initiative. They are added to the environ-

ment and it is up to the learners to decide on their use. Non-embedded support de-

vices are also called ‘‘tools’’. A tool could for instance be a button that enables the

learner to access additional information. The learners have to take action; they have

to click on the button before receiving additional information. In this contribution,

the latter kind of support devices, namely tools, are addressed.Given that the use of non-embedded support devices (tools) depends on the lear-

ner�s action, the integration of tools in learning environments presupposes, by def-

inition, that learners are good judges of their learning needs. Based on their

judgments, learners select tools when they need them. Learners control the use of

tools. Contrary to the assumptions that both providing learner control and allow-

ing learners to co-construct their learning environment establishes a ‘‘better’’ learn-

ing environment, a clear benefit of learner control on learning has not yet been

found (see reviews by Friend & Cole, 1990; Goforth, 1994; Large, 1996; Williams,1996). Most learner control studies report a positive effect on learner�s attitude,

whereas learning effects seem clearly mediated by various student characteristics.

Basically, these reviews conclude that commonly students experience difficulties

to make adequate choices for themselves (see also Clark, 1991; Hill & Hannafin,

2001; Land, 2000; Lee & Lehman, 1993; Milheim & Martin, 1991), i.e. choices ben-

eficial for their learning process. In other words, in an instructional context stu-

dents seem to lack self-monitoring and regulating skills. Applied to tools, it is

reasonable to expect that students will have problems to determine when they needhelp, what kind of help they need and hence, when the use of tools might be

beneficial.

A recent evaluation study indirectly validated this expectation (Clarebout, Elen,

Lowyck, Van den Ende, & Van den Enden, 2004). In this study, students did not

use the available tools when working on a diagnostic problem in a computer-based

learning environment. Thinking aloud protocols revealed that students thought they

would be cheating if they used the tools. In other words, students� instructional con-ceptions hampered students� judgment about the use of these tools. Similar resultswere published by Marek, Griggs, and Christopher (1999) for adjunct aids in text-

books. Students� conceptions about adjunct aids influenced the use of these aids, stu-

dents indicated to be less inclined to use those adjuncts aids promoting a more

elaborative study pattern.

These and similar studies raise doubt about the assumption underlying the ample

use of tools in learning environments: their use cannot be taken for granted. At the

same time however, from a constructivist view on learning, the use of open learning

environments is advocated to foster the acquisition of complex problem solving skills

G. Clarebout, J. Elen / Computers in Human Behavior 22 (2006) 389–411 391

(Jacobson & Spiro, 1995; Jonassen, 1997). These open learning environments

confront learners with a complex or ill-structured task to be solved by looking at dif-

ferent perspectives (Spiro, Feltovich, Jacobson, & Coulson, 1991). In open learning

environments students are in control and get different tools to their disposal to en-

gage in problem solving tasks. As a consequence, it becomes crucial to gain insightin (variables influencing) tool use. It might even be wondered whether these tools are

actually used and if they are used, whether they are used as intended.

Similar to research on adjunct aids, it can be hypothesized that different variables

will mediate the learning effect of tool use. Elen (1995) provides an overview of dif-

ferent variables mediating the effect of adjunct aids such as the learning task, the nat-

ure of the adjunct aid and, whether and when learners are urged to use the adjunct

aid. In this contribution, these variables will be addressed with respect to tool use in

computer-based learning environments. Additionally, the learning effect of tool usewill be addressed.

Through means of a literature study, an overview is presented of research on tools

in computer-based learning environments. 1 This overview is structured according to

questions relating to different variables that might mediate the effect of tool use on

learning.

First, the methodology will be discussed. Next, results are presented and finally

these results are reflected on. The conclusion offers possible solutions and sugges-

tions for further research.

1. Method

This literature study started with a search on the Web of Science and in the Psy-

cInfo database. 2 These databases were searched for the last 20 years (from 1982). It

can be argued that 20 years is a rather large interval for studies in computer-based

learning environment, given the evolutions in this domain. However, this allows toconsider also tool use in less complex learning environments and to see whether

the complexity of the learning environment plays a role in learners� tool use. Descrip-

tors (see Table 1) specifically relate to the use of tools or synonyms (options, adjunct

aids), and to environments in which tools are most likely made available (open learn-

ing environments, hypermedia environments). These descriptors were the results of a

brainstorm session by two researchers. Additionally, the initial results of this search

were presented on two conferences (Clarebout & Elen, 2002a, 2002b). The sugges-

tions raised by the audience were taken into account and entered in a new search.In all searches, the term ‘‘research’’ was added, since the aim of this study was to find

1 This contribution does not deal with the computer as a tool in itself, or more specific as a cognitive

tool (see Lajoie, 2000; Lajoie & Derry, 1993; Salomon, 1988).2 The search was performed June 2003 and updated in September 2004.

Table 1

Descriptors

Descriptors Web of science (SSI) PsycInfo

Option(s) use 1 3

Use of option(s) 12 15

Tool(s) use 45 141

Use of tool(s) 37 135

Open learning environment(s) 1 16

Electronic learning environment(s) 0 1

Hypermedia 137 246

Learner control 28 59

Instructional intervention(s) 19 86

Adjunct aid(s) 0 3

Discovery learning 15 37

Use of resource(s) 72 68

Resource(s) use 144 63

Inquiry-oriented instruction 1 1

Project-based environment(s) 0 2

Computer-assisted learning 31 26

Simulation* 127 178

Help use 23 11

Use of help 3 97

Scaffolds 13 53

Powerful learning environment(s) 1 6

Instructional option(s) 5 9

Instructional explanation(s) 8 4


research studies involving tool use rather than mere descriptions of tools. If the

search yielded too many results (N > 300), ‘‘learning’’ was entered as an additional

descriptor (marked with ‘‘*’’ in Table 1).

All abstracts were read to find out whether the publications dealt with research(qualitative or quantitative) on tools in computer-based environments at any level

of education. If this was the case, the publication was selected. Eventually, only

22 journal articles could be withdrawn. All 22 studies report research results on tool

use itself, variables influencing tool use and/or the effect of tool use on learning. No

review studies were included, as for instance the review on help seeking behavior by

Aleven, Stahl, Schworm, Fischer, and Wallace (2003). Providing the initial numbers

of records found, this limited number of selected articles seems very surprising. How-

ever, most studies provide a description of tools present in their learning environ-ment studied at hand. The studies do report research results, but these are not

related to the tools themselves. Other journal articles use the term ‘‘tools’’, but are

actually referring to embedded support devices or to computers themselves serving

as a cognitive tool.

In order to describe the different articles and to compare them, a classification sys-

tem of tools was looked for. Jonassen (1999) provides an elaborate categorization

system for support devices. This system is part of an instructional design model

for so-called ‘‘constructivist learning environments’’, i.e. learning environments thataim at fostering problem solving and conceptual development by confronting learn-


ers with ill-defined problems. This system provides a category of devices that visual-

ize, organize, automate or supplant thinking skills. Given the comprehensiveness of

this framework, it was used for classifying the different tools retrieved in the articles.

It should be noted that the Jonassen-system is not the only one. For instance, a sim-

ilar system is provided by Hannafin, Land, and Oliver (1999). However, their systemcan be completely integrated in Jonassen�s one.

Despite the elaborate system, and probably also due to the origin of the system,

one category was added based on the literature reviewed, namely ‘‘elaboration tools’’

(e.g., Carrier, Davidson, & Williams, 1985). It might be argued that elaboration tools

are knowledge modeling tools, however, elaboration tools are more directed towards

exercising rather than articulating or representing one�s knowledge.The following classification system is used:

Information resources: provide information students can use to construct their men-

tal models, formulate hypotheses and solve problems. These can be text documents,

graphics, video or animations helping students to understand the problem. Access to

the World Wide Web is an example of such a tool.

Cognitive tools: help students to engage in and facilitate specific kinds of cognitive

processing. These are devices to visualize, organize or supplant thinking skills

(e.g., visualization tools such as concept maps or simulations).

Knowledge modeling tools: help students to reflect on their learning process. Thesetools provide an environment in which students have to articulate what they know

and what the relationships are between different concepts (e.g., semantic network).

Performance support tools: facilitate the problem solving process by performing algo-

rithmic tasks for the learners. This allows learners to focus more on higher order

cognitive tasks (e.g., calculator, database shells).

Information gathering tools: help students in seeking information so that they are not

distracted from their primary goal of problem solving.

Conversation and collaboration tools: are used in collaborative learning environmentto support students in their collaborative knowledge building process (e.g., e-mail,

videoconferencing).

Elaboration tools: give access to reviews and additional exercises and practices

related to the content of the task.

For all studies, the nature of the learning task and the tool, the number of sub-

jects, the dependent and independent variables and the results will be mentioned

(see Tables 2 and 3).

2. Results

This section is structured in line with the research questions. A first section relates

to the variables influencing tool use, namely student characteristics, kind of tool,

learning task and explicit encouragement. A second section discusses research

findings with respect to learning effects of tool use.

Table 2

Factors influencing tool use

Authorsa Kind of

tool(s)

Independent

variable(s)

Result

Carrier et al. (1985) Elaboration Ability Path analysis: partial b coefficient: .59/No descriptives

N = 28/6th graders

concept learning

Locus of control Path analysis: partial b coefficient: �.08/No descriptives

Carrier et al. (1984) Elaboration Learning style (field

(in)dependence)

Frequency of option selection (proportion of total)/Definitions: FI: .25; FD: .22/Expository

instances: FI: 017; FD: .25/Practice instances: FI: .25; FD: .20/Feedback: FI: .34; FD:

.35 ) v2 analysis: only difference for expository instances: v2 = 8.87, p<.05

N = 44/7th graders

concept learning

Carrier et al. (1986)

N = 37/6th graders

Concept learning

Elaboration Option type Amount of times selected: Paraphrased definition: M = 62.8, SD = 37.1/Expository instances:

M = 26.1, SD = 25.9/Practice instance: M = 31.5, SD = 30/Analytic Feedback: M = 34.5

SD = 25.9) ANOVA: F(3, 105) = 20.13, p<.05

Encouragement Amount of option use: Encouragement group: M = 38.8/No encouragement group: M = 25.4

)ANOVA: F(1,35) = 4.82, p<.05

Chapelle and Elaboration Prior knowledged Amount of tool use Purpose of tool use Extra information

Mizuno (1989)

N = 13/University

students concept

learning

perminute:

High: M = .09/min, SD = .06

On-going problem solving: High = 0%, low = 7%

Low: M = .14/min,

SD = .08) T test:

t = �1.34; df = 11, n.s.

High = 78%, low = 48%b

Advanced organizer

High = 7% Low = 32%

Others

Amount of tool use per

sentence

Reconfirmation

High = 11%, low = 9%

High = 4% Low = 4%

High: M = .13/sent, SD = .09

) Low: M = .28/sent,

SD = .14) T test:

t = �2.27; df = 11, p<.05

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Crooks et al. (1996) Elaboration Individual/co- Amount of total optional screens consulted: Individual group: M = 74%/co-operative group:

N = 125/

Undergraduates

concept learning

operative

Leanplus/Fullminus

M = 65%) ANOVA: F(1,124) = 4.92, p<.05, ES = .35

Amount of total optional screens consulted: Leanplus: 56%/Fullminus: 83%)ANOVA: F(1,124) = 51.96, p<.05; ES = .99

Crooks et al. (1998) Elaboration Individual/co- Amount of total optional screens consulted: Individual group: M = 75.14, SD = 12.99/Co-

N = 97/

Undergraduates

concept learning

operative

Leanplus/Fullminus

operative group: M = 74.92, SD = 12.31) ANOVA: Not significant (no statistics)

Amount of total optional screens consulted: Leanplus: M = 67.36, SD = 16.11/Fullminus:

M = 82.70, SD = 9.19) ANOVA: F(1,96) = 34.36, p<.05

Grasel et al. (2001) Knowledge Strategy modeling Amount of using additional information corrective: Strategy group: M = 15.88/Without

N1 = 24/N2 = 12/

University

students problem

solving

modeling

Instruction

strategy group: M = 9.13) Mann–Whitney U test: U = 8.0 p<.05

Amount of using additional information corrective: Instruction group: M = 8.17/Without

instruction group: M = 4.83 )Mann–Whitney U test: U = 8.0, p<.10c

Fischer et al. (2003) Cognitive/

information

resources

Elaboration

(Prior knowledge)

Leanplus/Fullminus

Although prior knowledge was low; information resources hardly used (no statistics

provided). Cognitive tool was used by all students (no statistics provided)N = 11/University

students problem

solving

Hannafin and

Sullivan (1995)

Amount of total optional screens consulted: Leanplus: 32%/Fullminus: 78%) ANOVA:

F(1,132) = 4.13, p<.05

Interaction effect with ability: Fullminus-Low ability:M = 76%, SD = 18.7%/Fullminus-High

ability: M = 79%, SD = 20.6%

Leanplus-Low ability: M = 19%, SD = 5.1%/Leanplus-High ability: M = 43%,

SD = 11.4%) ANOVA: F(1,132) = 12.36, p<.05

N = 133/9th &

10th graders

concept learning

(continued on next page)

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Table 2 (continued)

Authorsa Kind of tool(s) Independent

variable(s)

Result

Hasselerharm and

Leemkuil (1990)

Cognitive Learning style (field

(in)dependence)

45% used advisement; no significant effect. No statistics provided

No significant effect, no statistics provided

N = 110/

Secondary ed. st.

concept learning

Prior achievement

Hicken et al. (1992) Elaboration Leanplus/Fullminus Amount of total optional screens consulted: Leanplus: 32% (SD = 27)/Fullminus:

80% (SD = 25)) ANOVA: F(1,92) = 70.80, p<.05; ES = 1.79N = 111/

Undergraduates

concept learning

Lee and Lehman

(1993)

N = 162/

Undergraduates

concept learning

Information

resource

Learning style

(active/neutral/

passive)

Instructional cues

Selection frequency: Active: M = 0.86, SD = 0.66/Neutral: M = 0.82, SD = 0.72/Passive:

M = 0.58, SD = 0.62) ANOVA: F(2,161) = 2.64, n.s.

Selection frequency: With cues: M = .94, SD = .58/Without cues: M = .59,

SD = .73 ) ANOVA: F(1,161) = 9.81, p<.05

Interaction effect with learning style: With cues-active: M = .82, SD = .52/With

cues-neutral: M = 1.17, SD = 0.58/With cues-passive: M = 0.75, SD = .53/Without

cues-active: M = 0.90, SD = 0.78/Without cues-neutral: M = 0.72, SD = 0.67/Without

cues-passive: M = 0.72, SD = 0.68 ) ANOVA: F(2,161) = 5.55, p<.05

Liu and Reed (1994)

N = 63/College

students concept

learning

Performance

support/

Information

resource/

Learning style (field

(in)dependence)

Amount of total tool use: FD: M = 16.21/Fmixed: M = 29.28/FI: M: 24.84

) ANOVA�s for 5 support tools: n.s., results reported for use of index: F(2,62) = 2.54,

p = .09, no results reported for other 4 tools

Elaboration

Martens et al.

(1997)e

N = 51/University

students concept

learning

Cognitive Prior knowledge

Reading comprehen

sion skills

MANOVA: k = 0.88, p< .05: Prior knowledge enhancing effect on use performance

support tools F(1,50) = 6.12, p<.05/testing tools F(1,50) = 3.99, p<.05/orienting tools:

not significant, no F value

MANOVA: No significant effects/descriptives, no statistics

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Oliver and Hannafin

(2000)

Knowledge

modeling/

performance

support

(Kind of tool)f Knowledge modeling tools rarely used (no descriptives provided)/Performance support

tool used more frequently

N = 12/8th

graders

problem solving

Pedersen and Liu

(2002)

Cognitive tool Expert support Number of times notebook was used: Group 1 (modeling): M = 86.5, SD = 21.4/Group 2

(didactic): M = 42.9, SD = 35.3/Group 3 (help): M = 51.4, SD = 24.2 ) F = 14.5; p<.05;

ES (g2) = .32N = 66/6th

graders

problem solving

Relan (1995)

N = 109/6th

graders concept

learning

Elaboration training Total amount of review: Learner control complete LCC: Comprehensive training: M:

3.2, SD = 2.4/Partial training:M = 2.9, SD = 3.0/No training:M = 3.1, SD = 2.8/Learner

control limited (LCL) Comprehensive training: M = 4.2, SD = 3.6/Partial training:

M = 1.2, SD = 1.6/No training: M = 2.4, SD = 3.6

) ANOVA: Not significant within LCC/) ANOVA: Not significant over two groups

(no statistics presented)

Renkl (2002) Cognitive Prior knowledge Cluster analysis: Four clusters: (1) above average prior knowledge, high far transfer

performance, little instructional explanation use/(2) low prior knowledge, good transfer

performance, above average use of extended instructional explanations/(3) average prior

knowledge, average performance, little use of extensive explanations, frequent use of

minimalist explanation/(4) above average prior knowledge, under average transfer

performance, little use of instructional explanations

N = 28/Student

teachers problem

solving

Schnackenberg and

Sullivan (2000)

N = 99/University

juniors concept

learning

Elaboration Ability

Leanplus/Fullminus

Amount of optional screens: High: M = 25.04, SD = 15.57/Low: M = 20.15,

SD = 15.68) ANOVA: F(1,98) = 3,71, n.s.

Amount of total optional screens consulted: Leanplus: 35%/Fullminus: 68%) ANOVA:

F(1,98) = 30.42, p<.05; ES = 1.08


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Table 2 (continued)

Authorsa Kind of

tool(s)

Independent

variable(s)

Result

Viau and Larivee

(1993)

Elaboration Prior knowledge Amount of tool use: Regression analysis: Weak: M = 8.3, SD = 6.8, r = .43, p<.05/

Avarege: M = 11.4, SD = 9.1, r = .50, p<.05/Strong: M = 10.8, SD = 6.6, r = .30,

p>.05N = 70/College

students concept

learning

a The studies are alphabetically ordered according to the first author.b The results presented are taken from Table 6 (p. 39) of this article. However, the authors report different percentages in their text (p. 38), where they state

that 71% of the high level students use the tools for on-going problem solving and 67% of the lower level students.c The authors indicate this to be significant.d This was an evaluation study, as such no real independent variables was specified in advance.

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Table 3

Learning effect of tool use

Authors Kind of tools Dependent

variable

Results

Carrier and Williams

(1988)

N = 114/6ht graders

concept learning

Elaboration Performance Pearson correlation of option selection and post-test: r = .28, p<.05;

with delayed test: r = .20, p < .05. Low opt. sel.: Mpa = 4.7, SD = 2.8/

Md = 4.8, SD = 3.1/Medium-low opt. sel.: Mp = 5.6, SD = 2.9/

Md = 5.0, SD = 3.4/Medium-high op. sel.: Mp = 8.6; SD = 2.4/

Md = 7.0; SD = 3.7/High opt. sel.: Mp = 6.9, SD = 3.7/Md = 6.5,

SD = 3.8

MANOVA on repeated measures: Interaction between treatment and

quadratic level of choice: F(1,86) = 4.28, p<.05; Interaction time by

level of option selection: Quadratic trend interaction: F(1,86) = 4.83,

p<.05

Carrier et al. (1985)

N = 20/6th graders

concept learning

Elaboration Performance Fisher�s exact test: High ability high option: post-test = .04;

delayed = .11/High-ability low option: post-test = .80, delayed = .80;

Low ability high option: post-test = .38; dealyed = .51/Low ability low

option: post-test = .11, delayed = .34

Martens et al. (1997)

N = 51/University

students concept

learning

Cognitive Performance Interaction between discernability and use of toolsbDiscernability and

use of processing tool: (F(1,42) = 5.66, p<.05)/Discernability and use

of testing tool: F(1,41) = 3.6, p>.06

Morrison et al. (1992) Elaboration Performance Correlation between tool use and performance: R = �.06; p>.05

N = 73/6th graders

concept learning

Attitude Correlation between tool use and attitude: R = .05; p>.05


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Table 3 (continued)

Authors Kind of tools Dependent

variable

Results

Oliver and Hannafin

(2000)

Knowledge monitoring Use of higher

order skills

Qualitative analysis: No effects found (little use): )no descriptives

given

N = 12/8th graders

problem solving

Renkl (2002)

N = 48/student

teachers problem

solving

Cognitive Performance One-tailed t test: t(46) = 1.71; p<.05/Post-test: Mc = 42.5, SD = 21.19;

ME = 53.71, SD = 23.30; ES = .50/Near transfer: Mc = 54.40,

SD = 26.25; ME = 63.93, SD = 28.85; ES = .34/Far transfer: Mc = 35.0,

SD = 20.22; ME = 47.32, SD = 22.85

Viau and Larivee

(1993)

N = 70/College

students concept

learning

Elaboration

Regression analysis: Frequency

glossary consultation: Weak:

M = 8.33, SD = 6.77; r = .43;

p<.05/Average: M = 11.36,

SD = 9.08; r = .50, p<.05/Strong:

M = 10.75, SD = 6.63; r = .30,

p<.05. Time on glossary: Weak:

M = 11.46, SD = 11.29; r = .29,

p<.05/Average: M = 16.26,

SD = 16.07; r = .39, p<.05/

Strong: M = 12.11, SD = 7.62;

r = .54, p<.05

Performance Multiple regression analysis: 21.6% of variance explained by frequency

of glossary consultation/Significant contribution of time (r = .32) and

frequency (r = .44) of glossary consultation. No significant contribution

for time (r = .08) or frequency (r = .12) of navigation map consultation

a Mp the mean score on post-test; Md the mean score on delayed test.b These groups consisted also of students receiving a printed version of a textbook.

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2.1. Variables influencing tool use

2.1.1. What student characteristics influence tool use?

Research on adjunct aids in textbooks suggests that different student characteris-

tics influence the use of tools (Elen, 1995). In 10 of the retrieved studies ability, priorknowledge, motivation, reading comprehension skills, locus of control and learning

style were studied.

These 10 studies show that some student characteristics have been considered as

influencing variables for tool use. However these studies appear to be inconclusive.

The effect of ability seems not stable. Two of the studies (Carrier et al., 1985; Cha-

pelle & Mizuno, 1989) found high ability students to profit more from control over

tool use than low ability students. 3 High ability students used the tools more fre-

quently than low ability students. Moreover, Chapelle and Mizuno (1989) showedthat high ability use tools differently than low ability students. High ability students

use tools as problem solving aids, while low ability students use these tools as ad-

vance organizers.

In contrast to Chapelle and Mizuno (1989), who indicated that the effect of ability

is only related to one specific tool (consultation of facts-tool) and not to the consul-

tation of a grammar-tool or dictionary, Schnackenberg and Sullivan (2000) could

not replicate the influence of ability on tool use.

Prior knowledge also is a non-stable factor. Martens, Valcke, and Portier (1997)observed a positive effect of prior knowledge on tool use in a computer-based text-

book, with more prior knowledge resulting in more tool use. Viau and Larivee (1993)

however, report a curvilinear relation. In their study, average students used the avail-

able tool more often than both weak and strong students. At the same time, Renkl

(2002) found low prior knowledge students to demand more frequently a tool pro-

viding instructional explanations than students with high prior knowledge. Again,

these results may be related to the nature of the tool. Martens et al. (1997) and Renkl

(2002) used cognitive tools, whereas Viau and Larivee (1993) used an elaborationtool.

Martens et al. (1997) also studied the effect of motivation and reading comprehen-

sion skills on tool use. No significant effects were found for these two characteristics.

Effects were neither revealed for locus of control. Carrier, Davidson, Williams,

and Kalweit (1986), showed no different tool use behavior between students who per-

ceive personal success or failure as a result of their own action (internal locus of con-

trol), and students who ascribe success or failure to external factors (external locus of

control).Learning style finally, has two meanings in the selected articles. One study denotes

activity level as learning style and differentiates between active, neutral and passive

learners (Lee & Lehman, 1993). No effects were found. All other studies measure

learning style as field (in)dependence (Carrier, Davidson, Higson, & Williams,

1984; Hasselerharm & Leemkuil, 1990; Liu & Reed, 1994). These studies are not

3 The correlations and differences reported are significant on a. 05-level.


conclusive: some find no effect at all (Hasselerharm & Leemkuil, 1990); in others field

independent learners tend to more frequently use an index tool than field dependent

learners, while the mixed group more frequently used a note-taking tool (Liu &

Reed, 1994). And, Carrier et al. (1984) report field independent learners to more fre-

quently use an elaboration tool than field dependent learners.Clearly, with respect to the influence of student characteristics on tool use the pre-

sented studies do not lead to a firm conclusion. The number of studies is limited and

only a restricted number of student characteristics has been investigated so far. How-

ever, the results do suggest that the nature of the tool might be important given the

interaction effects and the difference in use for different kind of tools. This aspect has

attracted specific research attention as will be presented in the next section.

2.1.2. Does the nature of the tools influence tool use?

Nine studies report on the influence of the nature of the tool on tool use. In the

previous part, three studies already pertain to this issue (Carrier et al., 1986; Chapelle

& Mizuno, 1989; Schnackenberg & Sullivan, 2000). Chapelle and Mizuno (1989) re-

vealed that the use of the glossary had a positive effect but the use of a navigation

map had no effect. Similarly, Carrier et al. (1986) showed that paraphrase tools were

more frequently used than elaboration tools. Schnackenberg and Sullivan (2000)

found that a tool to bypass instruction was used less often than a tool to request

additional information.The study of Chapelle and Mizuno (1989) confirms that the nature of the tool

matters: a glossary was used, a navigation map was not used. Oliver and Hannafin

(2000) made a similar conclusion based on a study in which they provided students

with different kinds of tools: performance support-, information gathering-, cogni-

tive- and knowledge monitoring tools. Students almost exclusively used the perform-

ance support and information gathering tools. Fischer, Troendle, and Mandl (2003)

likewise found the use of tools to be related to the kind of tool. In their study, the

cognitive tool (a visualization tool) was used, whereas information resources wereonly seldom used.

A large group of studies in this group studies differences between fullminus and

leanplus conditions (Crooks, Klein, Jones, & Dwyer, 1996; Crooks, Klein, & Sav-

enye, 1998; Hannafin & Sullivan, 1995; Hicken, Sullivan, & Klein, 1992; Schnacken-

berg & Sullivan, 2000). Such studies compare students who have access to a tool that

allows them to bypass instruction (fullminus) or students who have access to a tool

that gives them more instruction (leanplus). The additional instruction consists of re-

views, summaries and practice items (elaboration tools). In all these studies, the full-minus group views significantly more instruction than the leanplus group. Fullminus

groups only seldom use the tool to bypass instruction and leanplus groups seldom

request additional instruction.

Crooks et al. (1998) attribute the difference between the leanplus and fullminus

group to one elaboration tool, namely the consultation of practice items. No differ-

ences were found for any of the other elaboration tools. Carrier et al. (1986) also

found a difference in use between different tools in the leanplus groups: paraphrased


definition, expository instances, practice instances and analytic feedback. Para-

phrased definitions were used more often than expository instances. Similarly, prac-

tice items were more frequently used than expository instances.

Hannafin and Sullivan (1995) did not report specific differences between the ef-

fects of various elaboration tools, but they did find an interaction effect betweenability and the program version. In the leanplus version, but not in the fullminus

version, high ability students selected more (43%) options than low ability students

did (19%).

2.1.3. Do learning task and working method influence tool use?

None of the studies directly addresses the issue of learner tasks. In order to answer

this question, the tasks used in the different studies were looked at and compared to

the results with respect to tool use. In 16 studies, subjects had to learn specific con-cepts. Learning results were measured by a knowledge post-test. For instance, in the

studies of Carrier et al. (1984, 1985, 1986) subjects are confronted with a computer-

based lesson about four propaganda-techniques used in advertisement. After the les-

son, subjects were tested through means of a classification test. Only four studies deal

with problem solving tasks (Grasel, Fischer, & Mandl, 2001; Fischer et al., 2003;

Oliver & Hannafin, 2000; Pedersen & Liu, 2002).

In both groups of studies, results indicate that students tend to use some tools

more than others (see previous section) and hence, not all tools are used. It couldbe expected that in the problem-solving studies, subjects would need more tools, gi-

ven the more open character of the learning environment. However, the reviewed

studies do not confirm this expectation. These studies do not allow to draw firm con-

clusions of task influence on tool use.

Crooks et al. (1996, 1998) addressed the issue of working method. They investi-

gated the influence of individual versus co-operative work on the use of tools. The

1996-study revealed individuals to more frequently use optional elements than co-

operative groups. In the 1998-study, however, no differences were found betweenthe two working methods.

2.1.4. Does explicit encouragement of tool use affect tool use?

Advice while students are working with an application has a significant positive

effect (Carrier et al., 1986; Lee & Lehman, 1993). Students, who receive instructional

cues or encouragement to use certain options, use the available tools more compared

to students who do not receive these cues or encouragement. However, Lee and Leh-

man (1993) point to an interaction effect. A positive effect of encouragement seemedto apply only for regularly active learners, not for active or passive learners. Regu-

larly active learners with instructional cues selected more information than learners

with the same learning style without instructional cues.

Grasel et al. (2001) showed that students who received strategy training made

more adequate use of additional information (a glossary), a diagnostic help tool

and a database than students who did not receive strategy training. Additionally,


students in the strategy-training group adapted their problem solving process on the

basis of the additional information requested. To complicate matters, Relan (1995)

found that trained students used more frequently the elaboration tool before practice

than during practice, in contrast to the group who did not receive any training. It can

be questioned whether these mixed results are due to the difference in task or the nat-ure of the tools.

Finally, Pedersen and Liu (2002) studied the use of a notebook in a problem-

based learning environment. All students received additional support by an expert

on video. In the condition where the expert models his reasoning process by intro-

ducing and applying strategies and actually using the tools, the use of the notebook

was highest as was the number of relevant notes in that notebook. The other two

groups had an expert only providing information on how the tool functions, but

not using the tools. In addition, one group received suggestions for specific strate-gies. These two groups did not differ from one another.

Table 2 summarizes the results with respect to variables affecting tool use.

2.2. Learning effects of tool use

Whereas in the previous section variables influencing tool use were discussed, this

section addresses the effect of tool use on learning. Six out of 21 studies on tool use

report learning effects. Three of these studies deal with the effect of elaboration toolson learning (Carrier et al., 1985; Carrier & Williams, 1988; Morrison, Ross, & Bal-

dwin, 1992; Viau & Larivee, 1993); two other pertain to cognitive tools (Martens

et al., 1997; Renkl, 2002) and, one to the influence of knowledge monitoring tools

(Oliver & Hannafin, 2000). These studies provide mixed results on the effect of tool

use on performance.

Viau and Larivee (1993) showed that the use of an elaboration tool (glossary) ex-

plains 21.6% of the variance in performance results. They did not find this effect for

the use of a navigation tool, which suggests that an elaboration tool may have moreinfluence on performance than a processing tool. Carrier and Williams (1988) indi-

cated a moderate effect of tool use on performance. Moreover, this effect was medi-

ated by ability. High ability students benefit more when using the tools than low

ability student. However, they did have some statistical problems since only few

lower ability students actually used many tools.

Morrison et al. (1992) investigated the difference between a learner control and a

program control group. The learner control group performed worse than the pro-

gram control group. This group did not often use the elaboration tool present. Thislead the researchers to calculate the correlation between tool use and post-test score

for the learner control group. They could not find any significant correlation.

By using a higher order skill test, Oliver and Hannafin (2000) were unable to

reveal an effect of the use of performance tools on higher order learning. It has to

be noted that this is the only study where no knowledge test was used.

Martens et al. (1997) compared two groups, one in which students had access to

cognitive tools (discernability group) and one group where these tools were totally


integrated in the environment after students activated them (non-discernability

group). They report an interaction effect between the groups and the students� useof the support devices. Students who seldom used the elaboration and cognitive tools

scored higher on a post-test than students who frequently used these tools in the dis-

cernability group. The opposite was found for the non-discernability group.Renkl (2002) compared a group who did not get access to instructional explana-

tions with a group that did get access to these explanations when clicking on a but-

ton. He found that the learners in the experimental group were significantly more

successful in the post-test than the participants in the control group. A further anal-

ysis showed this effect to be due to the scores on the far transfer test rather than on

the near transfer test. In this study, four clusters of users could be identified. A first

cluster are students with high prior knowledge and a high gain on the transfer test

although they rarely relied on the instructional explanations. A second cluster of stu-dents had low prior knowledge, but had an overall good transfer performance and

they made often use of the extensive instructional explanations. A third cluster are

students with average prior knowledge and an average score on the transfer test.

They used the minimalistic explanations very frequently, but not the extensive ones.

In a last cluster those students could be classified with also high prior knowledge, but

only average transfer performance. They rarely sued the instructional explanations,

although the results on the post-test shows that they might have benefited from this

use.These results do not give a clear picture of the learning effects of tool use, but they

do give some indication that positive effects of tools cannot be taken for granted.

This seems to be related to the tools themselves, the way students use them, and spe-

cific student characteristics.

Of course, in order to find an effect of tool use on learning, students have to

use the tools (adequately). Clearly, this is one of the methodological problems

of studying the effect of tool use. For instance, Oliver and Hannafin (2000) report

that while cognitive tools and knowledge monitoring tools were provided, studentsonly seldom used them. Moreover, if students used these tools, they did not use

them as intended. For example, knowledge monitoring tools were developed and

integrated to promote higher order thinking processes, to organize information

or to justify ideas. However, students used these tools to make lists of web pages.

This could also explain the lack of positive effects of tool use on higher order

reasoning.

Table 3 summarizes an overview of the different studies reporting on learning

effects of tool use.

3. Discussion

Up to now the use of tools has attracted only minimal research attention. The

search revealed 17 studies addressing factors influencing tool use, and five studies

dealing with learning effects of tool use.


Although the analysis does not reveal a clear and convincing picture, some pre-

liminary conclusions can be drawn: tool use seems to be influenced by (1) student

characteristics, (2) the kind of tool, and (3) additional advice. Looking at the learn-

ing effect of tool use it is striking that only a small number of studies deal with the

influence of tool use on performance. Oliver and Hannafin (2000) aimed at study-ing this effect, but could not draw any conclusion since students hardly used the

tools. If the effects of tool use on performance are studied, only learning results

are looked at, the learning process itself and the effects of using these tools on

the learning process are addressed in one study only. This might be related to

the kind of task students are confronted with in these studies, namely a concept

acquisition task (see Tables 2 and 3). After working in this environment a knowl-

edge test is administered to measure whether students know these concepts. These

findings also raise the question whether there was a need for students to make useof the different tools in order to complete the task. Moreover, the reported studies

often lack a theoretical basis on which the inclusion of tools in the learning envi-

ronment and the measurement of particular (influencing) variables can be justified.

Performing a thorough task analysis before studying tool use might solve this

problem.

Providing that open learning environments foster the acquisition of complex

problem solving or higher order reasoning skills (Jacobson & Spiro, 1995; Jonassen,

1997), the research analysis, therefore, calls for a systematic research program; as in-sight in tool use becomes of particular interest with the use of open learning environ-

ments. In open learning environments, the learners construct their own knowledge in

interaction with the environment. In other words, there is a high level of learner con-

trol and learners have to regulate their own learning (Hannafin, 1995). This also in-

cludes making adequate choices towards tools to be used. In designing such

environments, it is important to understand the process of tool use. This also means

that not only the amount of tool use should be considered, but also what students

actually do when using tools (e.g. Grasel et al., 2001).However, in order to do so, some requirements have to be met:

(1) Students should first use tools before the adequacy of the actual use can be

studied. Studies indicate that this is not always the case. For instance, some of the

studies in which fullminus and leanplus groups are studied found either no effect

between these groups on performance (e.g. Hannafin & Sullivan, 1995; Crooks

et al., 1998) or they found the fullminus group to outperform the leanplus group

(e.g. Crooks et al., 1996; Schnackenberg & Sullivan, 2000). These findings are ex-

plained by referring to the number of tools used. The leanplus group almost didnot use the available elaboration tool. In a similar effort, Morrison et al. (1992)

revealed that the learner control group had reviewed only a limited number of

items. From the possible total of 12 review items, 42.3% reviewed three items,

40% four or five items and only one person reviewed them all (N = 73). There

seems to be evidence that merely providing tools to students does not result in

the use of these tools (e.g. Oliver & Hannafin, 2000; Fischer et al., 2003). However,

studying the adequacy of tool use also implies some requirements of the method-

ology used. While most studies use log files to track tool use, only the number of


use or kind of tool is looked at. The analysis of the log files on the level of when

these tools are used was not found. In three studies some other valuable methods

were used that allow gaining insight in the adequacy of tool used. Renkl (2002)

used thinking aloud procedures that allow to link tool use with participants� cog-nitive processes. Similarly, Oliver and Hannafin (2000), and Fischer et al. (2003)made use of observations to gain insight in tool use.

(2) A second requirement to study the adequacy of tool use relates to the relation

between the tool and the task. The studies investigate tool use, but the adequacy of

tool use for learning or the need for students to consult these tools when solving the

task is never questioned. An evaluation of the tools themselves, in view of the task is

required to establish the need for using these tools. Apparently, this cannot be taken

for granted. Morrison et al. (1992), for instance, did not find any correlation between

tool use and scores on a post-test. Similar results were found by Martens et al.(1997). However, with respect to the latter study it should be noted that tool use

was measured through a questionnaire that was administered after students worked

with the environment. It might be that students did not adequately report on their

actual tool use.

4. Conclusion

This contribution has reported on different studies addressing tool use. Some indi-

cation is provided of variables influencing learning and of the effect of tool use on

learning. Although only a limited number of studies were retrieved, a resemblance

with factors influencing the use of adjunct aids in texts was found.

When advocating the use of open learning environments, the issue of tool use be-

comes more apparent. Learners are in control and decide autonomously on the use

of these tools. Reviews on learner control and some of the studies reviewed, reveal

that students hardly apply monitoring and regulation skills for their learning processor for making adequate choices with respect to their learning. The limited number of

studies addressing tool use in computer-based learning environments indicates that

more research is needed to identify the different learner, tool and task characteristics

that affect tool use. For instance, a student characteristic that was not considered,

but that might be of relevance to tool use are students� conceptions about these tools.Winne (1985) already indicated that the functionality students ascribe to certain ele-

ments in a learning environment influence whether and how they will use these ele-

ments. This was already illustrated in the introduction. Similar, interviews by Brushand Saye (2001) clearly show that students do not use support devices as intended

because students were unsure how to use them. This also illustrates that the problem

not only relates to tools but to embedded support devices as well. Greene and Land

(2000) reveal that even when support devices are integrated in the environment, stu-

dents tend to use them inadequately. They provided questions to students to encour-

age deep-level processing. Instead of using these questions as a tool to aid cognition,

students responded with a superficial activity rather than with underlying cognitive


processes. This may explain why the provision of additional advice results in positive

effects.

Further research might identify how the optimal use of tools can be fostered.

Elaborating on the issue of providing additional advice or training can do this.

But also task, tool and student characteristics should be studied, such as students�conceptions or metacognitive skills. Different authors acknowledge the importance

of metacognitive skills for making adequate decisions (de Jong & van Joolingen,

1998; Hill & Hannafin, 2001; Kinzie & Berdel, 1990; Land, 2000; Lee & Lee,

1991). Hence, it can be expected that metacognitive skills are related to the sensible

use of tools.

Moreover, most studies in this review deal with elaboration tools, and only a lim-

ited number deal with other kind of tools, such as cognitive or knowledge modeling

tools. In order to be able to draw conclusions for the design of open learning envi-ronments, more research is needed in which students are confronted with problem

solving tasks, rather than concept acquisition tasks.

In fact, the same suggestions can be made for studying tool use, as Mayer (1979)

made for the study of advance organizers: ‘‘Future theories should attempt to specify

exactly what are the ‘‘subsuming concepts’’ in the advance organizer, how they are

related to the instructional information, and how the learning outcome of an ad-

vance organizer subject differs from the cognitive structure acquired by someone

who learns without an advance organizer’’ (Mayer, 1979, p. 163). This citationpoints also to the necessity of theories underlying the implementation of tools in a

learning environment.

In Fig. 1 an attempt is made to give an outline for further research with respect to

the use of tools. In this figure different aspects that can be involved in further

research are presented.

Taskcharacteristics

Toolcharacteristics

Studentcharacteristics

Additionalcues

quantity of tooluse

quality of tooluse

learningresults

Learning process

Fig. 1. Research outline.


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Documents

Tool use in computer-based learning environments: towards a research framework