RECREATIONAL VS. EDUCATIONAL COMPUTER EXPERIENCE: PREDICTING EXPLICIT AND IMPLICIT LEARNING OUTCOMES IN A. WEBSITE SEARCH.
RECREATIONAL VS. EDUCATIONAL COMPUTER EXPERIENCE: PREDICTING EXPLICIT AND IMPLICIT LEARNING OUTCOMES IN A WEBSITE SEARCH Webquests are becoming a popular teaching tool. “A WebQuest is a self-contained, inquiry-oriented activity constructed in the form of a Web page” (Descy, 2003, p. 363). During such tasks, students are exposed to a substantive amount of information, some of it relevant to a specific learning objective, some of it peripheral to that objective. Learning that occurs as a byproduct of another activity is referred to as incidental learning (Marsick & Watkins, 2001). Incidental learning is not purposeful learning, that is, it does not require the conscious intention or attention of the learner for learning to occur. Material learned incidentally may later be retrieved for explicit cognitive processing (Stadler, 1997) suggesting there is value in promoting incidental learning. Incidentally learned material may also exist as implicit knowledge, that is, knowledge the learner is unaware of and cannot directly report but knowledge that becomes apparent under certain test conditions. Previous research has identified relationships between individual skills and incidental learning from websites. For example, Baylor, (2001) reported that sensation seeking tendencies and spatial-synthetic ability (“one’s ability to perceive the whole picture from only the parts”, p.232) can impact different aspects of incidental learning. Spatial visualization skills, short-term memory span, and reading skills have also predicted incidental learning from a website (Boechler, Steffler, Dawson & Mansour, 2005; Boechler, Leenaars, Levner & Steffler, 2006) This study investigates the role of another individual skill on website search, prior computer experience. The key question of interest in this study is: Is there a relationship between certain prior computer experiences and retention of specific information from a website? For instance, video games are predominantly image-based with little accompanying text. Do users with high video game experience remember images better? Other computer activities, (e.g. word processing, using spreadsheets) are very text dependent. Do users that have more experience with these computer-based activities have better scores on tests that measure memory for the text from a website? Besides influences of individual skills on website search, research has also shown that on-screen features can impact incidental learning. Cress and Knabel (2003) reported that pop-up previews enhanced incidental learning. Baylor (2001) found that the presence of distracting links or a linear navigation mode can negatively impact incidental learning. Task instructions can also influence a website search. Wood, Kakebeeke, Debowski and Frese (2000) found that specific instructions to web search participants increased intrinsic motivation for the task. “These instructions encouraged participants to explore the database, to test alternative approaches, and not to worry about making mistake and errors . It was stressed that exploration and making mistakes was a natural part of the learning process” (p. 270). Therefore, in the current study, additional research questions regarding the influence of the learning context are: Is there a relationship between the presence of salient material in a website and incidental learning? Is there a relationship between the presence of instructions to attend to website material and incidental learning? This study investigates the incidental learning of material in a website during an information search task in relation to prior computer experience, learning instructions and salience of on-screen information. Methods Participants in this study were 109 undergraduate psychology students. The study contained four learning conditions based on two dimensions; salience of material and the instructions given. In the conditions labeled “salient”, students viewed webpages where an object appeared in an image and was also highlighted in the text (larger font and italicized) in the accompanying paragraph. In the conditions labeled “not salient” the object appeared in the image and the target word was also in the text but appeared in the same font size and type as all the other text. Two of the groups received the following instructions: “I would ask you to pay careful attention to and try to remember the information that you are viewing during the website search, as you will need to rely on it to complete the subsequent tasks”. The other two groups received no instructions to remember the material. We were also interested in the possible effects of individual skills students might have, particularly, their prior computer experience on incidental learning from a website. Therefore, students completed a computer quiz that had three parts; recognition of current software (recreational and educational) as a measure of general computer exposure, number of education-related computer activities they had carried out (e.g., creating a powerpoint presentation, using a formula in a spreadsheet, using a library database) as a measure of educational computer use, and the number of hours they spend playing video games as a measure of recreational computer use. The dependent variables representing learning outcomes were: an image recognition test, a multiple choice test (explicit knowledge), and a word completion test (implicit knowledge). Tasks
Students were asked to answer 15 questions by searching through a website about historical events. Students’ website search performance was measured by the total time to complete the search, the number of pages accessed and the accuracy of locating the target information for each of the 15 questions. As a general measure of exposure to computers we used a software recognition task. The software recognition task was based on the premise of Stanovich and West’s (1989) Title Recognition Test, which is a measure of print exposure. In this case, a student’s recognition of common software names was used as a general measure of exposure to computers. Students were asked to select from a checklist the software titles they recognized. The task contains 20 actual software names and 20 foils, that is, software titles that are not real. The foils allow for the detection of guessing in the responses. As per Stanovich and West (1989), the scores for this task were calculated by subtracting false-positive responses from correct responses. As a measure of computer experience related to educational objectives, students were asked to complete a checklist of 18 activities often required to complete educational assignments (e.g., using a formula in a spreadsheet, using a library database). As a measure of recreational computer use, students were asked to estimate the number of hours they spent playing video games within a week. To investigate the learning outcomes potentially associated with the experiences and website features mentioned above, three measures of learning were taken. First, memory for the images associated with the target information was assessed with an image recognition test consisting of 15 actual images and 15 foils. Students were simply asked to respond yes or no to the question: Did this image appear in the website you just viewed? Second, students completed a word-completion test (15 items) for the keywords that were salient in some of the conditions but not salient in others. This was a measure of implicit memory for the website material. Third, students completed a 15 item multiple choice test which was a measure of their explicit memory of the website material. The questions were drawn from material on the webpages where the target information appeared. Results Table 1 gives the means and standard deviations for each group for each learning variable. Table 1. Means and Standard Deviations for the learning measures by learning context. Dependent Variable Image Recognition Max = 15 Multiple Choice Test Max = 15 Word-Completion Test Max = 15
Learning Context
mean
SD
n
NotSalient/Instructions Not Salient/No-Instructions Salient/Instructions Salient/No-Instructions NotSalient/Instructions Not Salient/No-Instructions Salient/Instructions Salient/No-Instructions NotSalient/Instructions Not Salient/No-Instructions Salient/Instructions Salient/No-Instructions
14.36 14.87 14.19 14.92 8.23 7.80 8.32 7.65 5.50 7.03 9.35 8.96
1.59 2.11 2.02 2.02 2.27 2.26 2.57 2.56 2.96 2.90 2.48 2.79
22 30 31 26 22 30 31 26 22 30 31 26
Which aspects of prior computer experience predicted website search performance? Students’ website search performance was measured by the total time to complete the search, the number of pages accessed and the accuracy of locating the target information. These performance measures were analyzed by multiple regression, using as regressors; general computer exposure, education-related computer activities and video game playing. For the model predicting search accuracy, the regression was a rather poor fit (R2 = .12) but this was the only performance variable where the relationship to prior computer experience was significant (F3, 105 = 4.85, p = .003). The associations between search accuracy and each of the computer experience variables were as follows: General computer exposure, β = .27, t(105) = 2.68, p = .009, Education-related computer activities, , β = -.26, t(105) = 2.50, p = .014, and Video game play, , β = .20, t(105) = 2.13, p = .035. Note that education-related computer activities were negatively associated with search accuracy, that is, for every unit increase on the activity checklist, there was a .26 unit decrease in the search accuracy score. Also, of note is that the means for the image recognition scores are very high suggesting a ceiling effect, an indication the task was not difficult for most participants. Which aspects of prior computer experience and learning context predicted learning?
A series of regressions with planned contrasts were conducted to determine the role of group and prior computer experience in predicting the learning outcomes, that is the image recognition scores, the word completion scores and the multiple choice scores. The learning measures were analyzed by multiple regression, using as regressors; general computer exposure, education-related computer activities and video game play plus three group contrasts. Only the model predicting the word completion scores was significant, (R2 = .28, F6, 102 = 6.72, p < .000). For the computer experience variables, only the association between the word completion scores and general computer exposure was significant, β = .19, t(102) = 2.06, p > .000. For the group contrasts, the contrast that compared the NotSalient/Instructions Group with the Not-Salient/No-instructions Group was not significant suggesting that, in the absence of salient material, the presence of instructions to remember is not sufficient to increase word completion scores. The contrast that compared the Not-Salient/Instructions Group with the Salient/Instructions Group was significant, β = -.67, t(102) = 5.67, p > .000, suggesting that the presence of salient material contributes to the word completion outcomes. Finally, the contrast that compared the Salient/Instructions Group with the Salient/Noinstructions Group was significant as well, β = -.33, t(102) = 3.03, p = .003, suggesting that, when material is salient, the addition of instructions to remember adds to success on the word completion task. Conclusions In answer to the first two questions posed in the introduction: 1) No - high frequency video game players did not appear to remember images any better than low frequency players, however, given the ceiling effect on this task the results are inconclusive, 2) No - students who scored high on engaging in education-related computer activities did not score higher on the learning of text-based material in a website search (the word-completion test and the multiple choice test). However, general exposure to computers did predict the acquisition of implicit knowledge (word completion scores) but did not predict explicit knowledge (performance on the multiple choice test). Regarding the relationship to specific learning conditions, without drawing attention to specific material in a website, instructions to remember the material are not enough to support learning. Making select material visually salient does support incidental learning from websites. References Baylor, A.L., 2001, Incidental learning and perceived disorientation in a web-based environment: External and internal factors. Journal of Educational Multimedia and Hypermedia, 10, 3, 227-251. Boechler, P.M., Levner, I., Leenaars, L, & Steffler, D. (2006). Navigation and learning in educational hypermedia: Are poor readers at a disadvantage? Proceedings Cognition and Exploratory Learning in the Digital Age (CELDA) 2006, Barcelona, Spain: AACE Boechler, P., Steffler, D., Dawson, M., & Mansour, J. (2005). Incidental Learning in Hypermedia Environments: The Impact of Individual Differences and Spatial Overviews. In Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2005. Norfolk, VA: AACE Descy, D. E. (2003). Web-based Organizational Tools and Techniques in Support of Learning. Library Trends, 52, 2, 362-366. Stanovich, K.E. & West, R.F. (1989). Exposure to print and orthographic processing. Reading Research Quarterly, 21, 402–433. Wood, R. E., Kakebeeke, B. M., Debowski, S. & Frese, M. (2000). The impact of enactive exploration on intrinsic motivation, strategy, and performance in electronic search. Applied Psychology: An International Review, 49, 2, 263283.
