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Computers in Human Behavior xxx (2009) xxx–xxx
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Contents lists available at ScienceDirect
Computers in Human Behavior
journal homepage: www.elsevier.com/locate/comphumbeh
Online activity, motivation, and reasoning among adult learners
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3 Sarah Ransdell *
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4 College of Allied Health and Nursing, Nova Southeastern University, 3200 S University Drive, Fort Lauderdale, FL 33328-2018, USA
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a r t i c l e i n f o a b s t r a c t
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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
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Ó 2009 Published by Elsevier Ltd. 27
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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
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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
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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
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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
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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: ransdell@nova.edu 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

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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
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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
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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
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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-
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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
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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
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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
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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
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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
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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
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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
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Analysis
Pearson correlation .319a .212 1.000 .628b À.005
Sig. (2-tailed) .027 .149 .000 .973
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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
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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 (http://en.wikipedia.org/wiki/Baby_boomer). 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
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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
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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
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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.
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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
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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
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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