Plant identification is taught in horticulture and botany courses worldwide. Educators use visual tools to help students learn plant materials (Kling et al., 1996). Accessing live plant specimens to display variations in plant appearance at different growth stages and environments may be difficult (White et al., 1990), and reference books may not provide adequate visual plant information (Kling et al., 1996). Obstacles to teaching plant identification include not having live plant material in season, limited time spent in the classroom or laboratory to view slides, and insufficient comparisons of pictures per taxon (White et al., 1990).
These constraints have been reduced through use of videodiscs and computers, in which students interact through questions and guided prompts (White et al., 1990). Computer-assisted instruction has been used to help students learn the morphologic relationships between plants and learn morphology to identify unknown species (Shaw, 1993). Computer-based systems can also store large numbers of visual images in a format that is compact and affordable (Kling et al., 1996).
Several software programs have been developed to help students identify plants (Kling et al., 1996; Peffley et al., 1999a; Pohl, 1989; Sabota et al., 1995; Seiler et al., 2002). With the advent of these computer aids, it would be of interest to examine whether these programs are as effective in assisting students in identifying plants as traditional (classroom or laboratory)-based instruction.
In a study conducted by Seiler et al. (2002), computer instruction was superior to live instruction in teaching plant identification. Students who used a woody plant identification software program for a 2-week period increased their scores on weekly plant identification (ID) quizzes when compared with students receiving traditional review sessions with an instructor. In contrast, in each of three case studies, Taraban et al. (2004) reported higher plant ID quiz scores when students received live instruction on herbaceous or woody plants than when they received instruction through the web.
A few studies in plant identification have used photographs while testing students. Students obtained higher scores when completing plant ID tests involving photographs on the web when compared with taking tests in the laboratory with live plant specimens (Anderson and Walker, 2003). While offering an online tree identification class, Seiler et al. (2002) noted students were very successful in identifying tree species in previously unseen photographs on the computer. However, it is inconclusive from the literature as to whether web-based students can identify plants from photographs and live specimens on quizzes just as well as traditional course students.
Educators should be aware their students may represent a range of learning styles and that a learning sequence that is suitable for one student may not be suitable for another student (Bork and Gunnarsdottir, 2001). Therefore, teaching in such a manner that would satisfy all or most of those styles would increase learning in the classroom. Kahtz (2000) reported that supplementing a woody plant identification course with computer-assisted instruction was of equal benefit to field-dependent students (who take a “spectator” approach in learning and need more explicit instruction in solving problems) or field-independent students (who are more active by developing strategies to learn material lacking in structure) (Witkin et al., 1977). To better understand distance teaching and learning, research is needed on the relationship between learning styles and agricultural distance education (Miller, 1997). Such research was addressed in this study.
To achieve this goal, this study examined whether students obtained equivalent scores in a plant ID quiz after receiving live or web-based instruction in herbaceous plant identification. In addition, we investigated whether the two groups of students differed in identifying plants from live specimens and photographs, and examined whether learning style and demographic factors [age, gender, grade point average (GPA), level of computer use] were correlated to quiz scores for each mode of instruction.
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