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Chb 1185

  1. 1. CHB 1185 No. of Pages 4, Model 5G 5 October 2009 ARTICLE IN PRESS Computers in Human Behavior xxx (2009) xxx–xxx 1 Contents lists available at ScienceDirect Computers in Human Behavior journal homepage: Online activity, motivation, and reasoning among adult learners F 2 3 Sarah Ransdell * OO 4 College of Allied Health and Nursing, Nova Southeastern University, 3200 S University Drive, Fort Lauderdale, FL 33328-2018, USA 5 a r t i c l e i n f o a b s t r a c t 7 1 6 PR 8 Article history: College students’ motivational beliefs influence their online behavior and ability to think critically. In the 17 9 Available online xxxx present study, doctoral health science students’ reports of motivation, as measured by the California Mea- 18 sure of Mental Motivation, reasoning skill, as measured by the Health Science Reasoning Test, and Web- 19 10 Keywords: CT records of online activity during a Web-CT-based statistics course were explored. Critical thinking skill 20 11 Critical thinking dispositions and disposition each contributed unique variance to student grades, with age, organization disposition, 21 12 Critical thinking skills and analysis skill as the strongest predictors. The youngest students, those so-called millennial age, 22 13 Health science students and born after 1982, were those with the lowest critical thinking skill and dispositions, and the lowest 23 14 Online communication D 15 grades in the class. Future research must take into consideration discrepancies between skill and dispo- 24 sition and interactions with age or cohort. At present, and contrary to popular wisdom, older students 25 may make better online learners than younger. 26 TE Ó 2009 Published by Elsevier Ltd. 27 28 29 30 1. Introduction from postings to discussion. Some students may have critical think- 57 ing skills, but not the disposition to use them because of instructor 58 EC 31 Students’ motivation to think critically has been shown to im- requirements, or because online learning offers some latitude in 59 32 prove online learning (Cocea, 2006). One way to encourage critical how students may proceed. Giancarlo, Blohm, and Urdan (2004) 60 33 thinking is by motivating meaningful and frequent online discus- developed an assessment of critical thinking disposition called the 61 34 sion (Dennen, 2007). Dennen points out that online discussions California Measure of Mental Motivation (CM3). The CM3 yields four 62 35 may, however, only be indirectly connected to student learning theoretically meaningful dimensions, Learning Orientation, Creative 63 RR 36 (2007). The purpose of the present study is to compare student Problem Solving, Mental Focus, and Cognitive Integrity. The four fac- 64 37 performance among doctoral health science students in terms of tors are reliable across a wide range of Western samples, and are cor- 65 38 critical thinking disposition and skill in an online statistics class. related with known measures of student motivation and 66 39 The main research question is whether critical thinking will com- achievement. The present study compares the predictive power of 67 40 plement measures of online activity to anticipate learning among the CM3 and the Health Science Reasoning Test (HSRT). The HSRT 68 CO 41 graduate students ranging in age from 26 to 60. (Facione & Facione, 2006) was designed to measure critical thinking 69 42 Our recent research shows that students need to interact ac- skill, a necessary, but insufficient predictor of student success 70 43 tively with online resources for instruction to be maximally effec- (Giancarlo et al., 2004). HSRT questions do not require specific med- 71 44 tive (Ransdell & Gaillard-Kenney, 2009; Ransdell, Gaillard-Kenney, ical knowledge, but are stated in terms of real health care situations 72 45 & Weiss, 2007). Ransdell et al. (2007) found that the number of ori- maximizing the reliability and validity of the tool in this population. 73 46 ginal postings to discussion lists, but not the total count, was Nearly all health science students take a statistics course like 74 UN 47 among the best unique predictors of exam performance. Dennen the one in this study. The content is well-structured and relatively 75 48 (2007) cautions that quality must be supplemented with quantity stable over time. Some students may be less motivated to take it 76 49 in order to make sense of the myriad tracking functions automat- than other courses, but since it is part of the core curriculum, it 77 50 ically provided by Web-CT, Blackboard, and other tools like them. may be a good place to start in determining online activity, moti- 78 51 The present research addresses this issue of multiple markers of vation, and reasoning among adult learners. This research will also 79 52 online activity by testing a model including critical thinking dispo- describe any evidence for the often-found tendency of older college 80 53 sition, skill, and some of the most common measures of online learners to underestimate their own performance relative to youn- 81 54 activity, total hits, readings, and postings. ger learners (for a review see Tyler-Smith, 2006). 82 55 Because of the especially tight time constraints of many online There is some evidence that online learning requires even more 83 56 students, they need every motivation to participate in and benefit learner motivation and self-direction than traditional classroom- 84 based instruction (Bell, 2006). Berenson, Boyles, and Weaver 85 * Tel.: +1 954 262 1208; fax: +1 954 262 1181. (2008) found that older students taking online courses tended to 86 E-mail address: have higher dispositions to think critically and perform better than 87 0747-5632/$ - see front matter Ó 2009 Published by Elsevier Ltd. doi:10.1016/j.chb.2009.09.002 Please cite this article in press as: Ransdell, S. Online activity, motivation, and reasoning among adult learners. Computers in Human Behavior (2009), doi:10.1016/j.chb.2009.09.002
  2. 2. CHB 1185 No. of Pages 4, Model 5G 5 October 2009 ARTICLE IN PRESS 2 S. Ransdell / Computers in Human Behavior xxx (2009) xxx–xxx 88 younger students. Berenson et al. reason that the online environ- ings, and total original postings to discussion were automatically 136 89 ment may depend more on motivation than the traditional class- provided by the Web-CT program. The author also recorded these 137 90 room and therefore older students with higher motivation may three outcomes for those students who agreed to participant as 138 91 do better online than younger. Therefore, the present study will volunteers. 139 92 compare older students with younger students taking an online 93 course in terms of critical thinking skills and disposition. 2.3. Materials 140 94 The first hypothesis is that course grades will be higher for older 95 students which, of course, may be a proxy for cohort and experi- The CM3 was used to assess motivation and is based on the Cal- 141 96 ence. The second hypothesis is that online activity, and both criti- ifornia Critical Thinking Skills Test (Facione, 2000). The CM3 as- 142 97 cal thinking skill (as measured by the HSRT) and disposition (as sesses mental focus, learning, creative problem solving, and 143 98 measured by the CM3), will reliably predict course grades. Prof- cognitive integrity. Research shows the CM3 to have high reliabil- 144 99 etto-Grath (2003) has shown that nursing students have uneven F ity and to be predictive of standard self-efficacy measures (Gianc- 145 100 skill and disposition. Critical thinking dispositions were found to arlo et al., 2004). The HSRT (Facione & Facione, 2006) is a reliable 146 101 be high, but skill was lagging behind. In the present study, skill assessment of reasoning and critical thinking skills regardless of 147 OO 102 and disposition are predicted to provide independent sources of the specific area of expertise the respondent may possess. Health 148 103 explanation to exam performance and online activity. science students have been shown to be more motivated to per- 149 form at the level of their skill when the context includes everyday 150 104 2. Method health care examples. Table 1 shows the descriptive statistics for 151 the main subsets of the HSRT, Analysis and Evaluation and the 152 105 2.1. Participants main subset of the CM3, Orga, that are predictive of age and grade. 153 PR 106 Fifty-six graduate students in a doctor of health science statis- 2.4. Data analysis 154 107 tics course taught by the author were asked to volunteer for a 108 one-hour online assessment including the motivation assessment, The statistical analysis employed a linear regression analysis to 155 109 CM3, and the health science reasoning assessment, HSRT. Students determine the unique variance accounted for in student grades by 156 110 who did not choose to participate were given the option of writing age, online activity, and the CM3 and HSRT (i.e., Hoffman, 2004). 157 111 an extra one-hour assignment. Both student volunteers and non- D 112 volunteers received extra-credit in their class upon completion 3. Results 158 113 for one hour of time. All other class activities were as in the original TE 114 course. The research protocol was approved by the university’s Thirty-two percent of the variance in student learning was ac- 159 115 Institutional Review Board in accordance with the Declaration of counted for by a model including age, reasoning skill and disposi- 160 116 Helsinki. tion, and online activity. A multiple regression analysis revealed a 161 117 The average age of the 48 students who volunteered to partici- significant model for predicting students grades in the class, 162 118 pate was 42.9, SD = 10.0. Average HSRT was 17.8/33, just below the EC R = .56, F(6, 40) = 3.11, p < .05. Age and critical thinking disposition 163 119 50th%tile in a comparable national sample (SD = 4.9). Average CM3 to organize were the single best predictors, each with partial corre- 164 120 was 44.1, SD = 4.7, indicating that a majority of the students pos- lations of .30 (see also Table 2 for bivariate correlations of .34 and 165 121 sessed strong dispositions to think critically, at least as measured .25, respectively). Those students who were older, and self-re- 166 122 by the CM3. Skill and disposition were not reliably correlated. Half ported better organization, achieved better grades than those 167 123 of the students self-reported to be white and 2/3 were women. The who were younger. Analysis reasoning skill from the HSRT was 168 RR 124 average total number of hits to the website for course materials the next best predictor with .31, and Evaluation skill from the HSRT 169 125 and discussions was 687 over 15 weeks (SD = 228). The average predicting grades, .20 (see Table 2). Online activity in terms of to- 170 126 number of readings of discussion postings in the website was tals hits, readings, and postings to the web-based course yielded 171 127 273, SD = 116. The average number of original postings to discus- partial correlations of .18, .20, and .18, respectively. 172 128 sion was 6, SD = 7. The main learning outcome variable was the fi- For the purposes of description, participants were divided into 173 CO 129 nal grade before the final with a mean of 85%, SD = 5.5. Table 1 four groups of high skill and disposition, low skill and disposition, 174 130 shows the descriptive statistics for the main variables. low skill, high disposition, and high skill, low disposition based on 175 a median split of the CM3 and the HSRT. The youngest students, 176 131 2.2. Procedure the 11 who scored both low in disposition and skill, had an average 177 age 38.6, SD = 9.1 (HSRT, 12, CM3, 37), including all the millennial 178 132 Forty-eight student volunteers signed an informed consent and students. Eleven students overestimated their skill relative to their 179 UN 133 took the CM3 and HSRT online at their leisure during the 15 week disposition, were on average, 40.2, SD = 6.7, and tended to be men. 180 134 online statistics course offered through Web-CT, a web-based com- Twelve students underestimated their skill, were among the oldest 181 135 munication tool. Total hits, total readings of the discussion post- in sample (average = 48, SD = 9.8), and were more likely to be wo- 182 men. The 13 best performing students in the sample were, on aver- 183 Table 1 age, 44 in age, SD = 12, and equally men and women. 184 Descriptive statistics for main outcome variable ‘‘gradeb4final”, chronological age ‘‘age”, critical thinking skill ‘‘analysis” and ‘‘evaluation”, and critical thinking disposition to organize ‘‘orga”. 4. Discussion 185 Descriptive statistics Among graduate health science students in a relatively small 186 Mean SD N sample, critical thinking disposition and skill each contribute un- 187 Gradeb4final 85.0357 5.53654 56 ique, non-overlapping sources of variance to learning outcomes 188 Age 42.90 10.024 48 in an online statistics course. Critical thinking disposition was uni- 189 Analysis 3.60 1.425 48 formly strong among these mostly baby-boomer age students, but 190 Evaluation 4.58 1.381 48 critical thinking skill was widely distributed. Baby-boomer age is 191 Orga 41.9837 9.23964 47 typically defined as people who were born during the middle of 192 Please cite this article in press as: Ransdell, S. Online activity, motivation, and reasoning among adult learners. Computers in Human Behavior (2009), doi:10.1016/j.chb.2009.09.002
  3. 3. CHB 1185 No. of Pages 4, Model 5G 5 October 2009 ARTICLE IN PRESS S. Ransdell / Computers in Human Behavior xxx (2009) xxx–xxx 3 Table 2 Bivariate correlations among main outcome variable ‘‘gradeb4final”, chronological age ‘‘age”, critical thinking skill ‘‘analysis” and ‘‘evaluation”, and critical thinking disposition to organize ‘‘orga”. Gradeb4final Age Analysis Evaluation Orga Gradeb4final Pearson correlation 1.000 .343a .319a .206 .256 Sig. (2-tailed) .017 .027 .159 .083 N 56.000 48 48 48 47 Age Pearson correlation .343a 1.000 .212 .312a À.055 Sig. (2-tailed) .017 .149 .031 .713 N 48 48.000 48 48 47 F Analysis Pearson correlation .319a .212 1.000 .628b À.005 Sig. (2-tailed) .027 .149 .000 .973 OO N 48 48 48.000 48 47 Evaluation Pearson correlation .206 .312a .628b 1.000 .106 Sig. (2-tailed) .159 .031 .000 .477 N 48 48 48 48.000 47 Orga PR Pearson correlation .256 À.055 À.005 .106 1.000 Sig. (2-tailed) .083 .713 .973 .477 N 47 47 47 47 47.000 a Correlation is significant at the 0.05 level (2-tailed). b Correlation is significant at the 0.01 level (2-tailed). 193 the 20th century ( In- D Halpern’s (1990) model of critical thinking instruction was orig- 232 194 creased engagement in the online environment, as measured by to- inally informed by gender differences in critical thinking disposi- 233 TE 195 tal hits, readings, and postings, also contributed modest variance. A tion and skill, and in transfer across domains. The domain of the 234 196 surprisingly strong variable, chronological age, was as predictive as online course environment may demand even higher levels of crit- 235 197 was critical thinking disposition. Age may have served as a proxy ical thinking disposition and skill than traditional classrooms. Old- 236 198 for cohort, level of student experience, health care experience, or er students may be better equipped to muster those skills, or may 237 199 administrative experience. Contrary to popular wisdom, older stu- be more sensitive to meeting the unique demands of a self-directed 238 EC 200 dents may make better online learners than younger (i.e., Berenson learning environment. Future research must take into consider- 239 201 et al., 2008). ation discrepancies between skill and disposition. The source of 240 202 Among several orthogonal factors within critical thinking dispo- such discrepancies is likely to be related to the curricular require- 241 203 sition as measured by the CM3, organization, a component of Men- ments placed on students. As Dennen (2007) suggests, ‘‘all roads 242 204 tal Focus, was the strongest unique predictor of learning. These lead to learning”, but some students may be more motivated to 243 RR 205 students tend to agree with statements like ‘‘It is easy for me to use online discussion to aid critical thinking than others. 244 206 organize my thoughts”. Those students scoring low on organization 207 show a tendency toward disorganization and procrastination. In Uncited references 245 208 the present study, older students were also more inclined to stron- 209 ger critical thinking dispositions but tended to overestimate them (Giancarlo (2006)). Q1 246 210 if they were men and underestimate them if they were women. CO 211 Among three subscales of the HSRT, analysis, inference, and Acknowledgements 247 212 evaluation, that together form the major core skills identified in 213 critical thinking theory by The Delphi Report (1990), analysis is The author thank the students of DHS 8010, Statistics and Re- 248 214 the strongest unique predictor of student’s grades in the statistics search Methods, taking the course in the summer of 2008, for their 249 215 class. Analysis measures the ability to comprehend and express the valuable participation in this research. The author will also thank 250 UN 216 meaning of a wide variety of experiences, data, events, judgments, Rick Davis, Sandrine Gaillard-Kenney, Kathleen Hagen, Pat Kelly 251 217 and procedures which includes the subskills of categorization, and Brianna Kent for their assistance and insight. 252 218 decoding significance, and clarifying meaning. 219 The present students are presumably older than most college References 253 220 student samples. The oldest of these students were more likely 221 to self-report strong critical thinking dispositions, presumably Bell, P. D. (2006). Can factors related to self-regulated learning and epistemological 254 222 from a combination of educational and professional health care beliefs predict learning achievement in undergraduate asynchronous web- 255 based courses? Perspectives in Health Information Management, 3, 7–15. 256 223 experiences. The present correlational study cannot test this inter- Berenson, R., Boyles, G., & Weaver, A. (2008). Emotional intelligence as a predictor 257 224 pretation of older students, but online introductions by them sug- for success in online learning. The International Review of Research in Open and 258 225 gest a broader and deeper experience base upon entering the Distance Learning, 9, 1–18. 259 Cocea, M. (2006). Assessment of motivation in online learning environments. AH, 260 226 course. Success in the online statistics course was stated in the syl- 261 LNCS, 4018, 414–418. 227 labus as keeping up with the many homework assignments and Dennen, V. P. (2007). Looking for evidence of learning: Assessment and analysis 262 228 readings and, actively engaging in the online discussions. As some methods for online discourse. Computers in Human Behavior, 24, 205–219. 263 Facione, P. (2000). California Critical Thinking Skills Test. Milbrae, CA: Insight 264 229 justification for this syllabus statement, up to a third of the vari- Assessment, The California Academic Press. 265 230 ance was accounted for in the grades of this class by older students Facione, N., & Facione, P. (2006). The health sciences reasoning test. Milbrae, CA: 266 231 contributing more original postings to discussion than younger. Insight Assessment, The California Academic Press. 267 Please cite this article in press as: Ransdell, S. Online activity, motivation, and reasoning among adult learners. Computers in Human Behavior (2009), doi:10.1016/j.chb.2009.09.002
  4. 4. CHB 1185 No. of Pages 4, Model 5G 5 October 2009 ARTICLE IN PRESS 4 S. Ransdell / Computers in Human Behavior xxx (2009) xxx–xxx 268 Giancarlo, C. (2006). The California measure of mental motivation. Milbrae, CA: Ransdell, S., & Gaillard-Kenney, S. (2009). Blended learning environments, active 282 269 Insight Assessment, The California Academic Press. participation and student success. The Internet Journal of Allied Health Sciences 283 270 Giancarlo, C., Blohm, S., & Urdan, T. (2004). Assessing secondary students’ and Practice, 7(1). Avilable from 284 271 disposition toward critical thinking: Development of the California Measure Ransdell, S., Gaillard-Kenney, S., & Weiss, S. (2007). Getting the right blend: A case 285 272 of Mental Motivation. Educational and Psychological Measurement, 64(2), 347– study of how teaching can change in blended learning environments. eJournal of 286 273 364. Learning and Teaching, 2(2). Available from 287 274 Halpern, D. F. (1990). Teaching for critical thinking: Helping college students The Delphi Report, (1990). Critical thinking: A statement of expert consensus for 288 275 develop the skills and dispositions of a critical thinker. New Directions for purposes of educational assessment and instruction. The American Philosophical 289 276 Teaching and Learning, 80, 69–74. Association, ERIC, 315–423, 80. 290 277 Hoffman, J. P. (2004). Generalized linear models: An applied approach. Boston: Tyler-Smith, K. (2006). Early attrition among first time eLearners: A review of 291 278 Pearson. factors that contribute to drop-out, withdrawal and non-completion rates of 292 279 Profetto-Grath, J. (2003). The relationship of critical thinking skills and critical adult learners undertaking eLearning programmes. Journal of Online Learning 293 280 thinking dispositions of baccalaureate nursing students. Journal of Advanced and Teaching, 2(2). Available from 294 281 Nursing, 43, 569–577. 295 F OO PR D TE EC RR CO UN Please cite this article in press as: Ransdell, S. Online activity, motivation, and reasoning among adult learners. Computers in Human Behavior (2009), doi:10.1016/j.chb.2009.09.002