Academic journal article Journal of Information Technology Education

Knowledge Structures of Entering Computer Networking Students and Their Instructors

Academic journal article Journal of Information Technology Education

Knowledge Structures of Entering Computer Networking Students and Their Instructors

Article excerpt

Introduction

Students come to their learning experiences with a variety of ideas gained from their previous experiences. The ideas, skills, and abilities that students bring with them are referred to as prior knowledge (Jonassen & Gabrowski, 1993). Prior knowledge greatly affects how students understand, remember, and ultimately learn new information. As early as 1968, Ausubel noted, "the most important single factor influencing learning is what the learner already knows." As new information is presented, it is essential for it to be linked with information already known by the student, and it is these connections that will allow learners to access the new information later (J. R. Anderson, 1995). Because novices in a field by definition don't know the content in that field, they have little to which they can relate the things they're attempting to learn. In addition, students frequently don't make logical connections between new ideas and prior knowledge (Hayes-Roth & Thorndyke, 1979).

In many cases the domain-specific ideas that students carry with them are mistaken (Bishop & Anderson, 1990; Brown & Van Lehn, 1980; Costu & Ayas, 2005; Michael, 1998; Vosniadou & Brewer, 1992). Science, in particular physics, is one area where these mistaken ideas have been extensively explored. For example, it is known that students often think that force must be continuously applied to an object in order for the object to remain in motion and that heavier objects fall faster (Halloun & Hestenes, 1985a). Misconceptions like these can affect students' comprehension and memory of new material (Kendeou & van den Broek, 2005). Among introductory statistics students, the number of misconceptions held is negatively correlated with their class achievement (Khazanov, 2006).

Information about misconceptions of students in a class can assist the teacher in planning instruction. Helping teachers to understand students' knowledge and thinking leads to using improved instructional strategies (Carpenter, Franke, Jacobs, Fennema, & Empson, 1998; Cobb et al., 1991). Information about common misconceptions across groups of students can also assist instructional designers and content developers in creating materials that provide opportunities for students to challenge their existing misconceptions while linking new knowledge to existing knowledge.

Identifying Misconceptions

In order to use information about misconceptions to inform instructors and instructional designers, the misconceptions must be identified. There are several methods that have been explored to identify misconceptions among students, including instructor interviews, student interviews, and student assessments. Each of these has strengths and weaknesses with regard to the type of information they will produce, their ability to be used with large groups of students, and the amount of time and effort they take to complete.

Instructor interviews have been employed in the domains of statistics (Khazanov, 2006) and elementary students' physics knowledge (Pine, Messer, & St. John, 2001) to successfully develop lists of common misconceptions. The strength of this approach is that experienced instructors have seen many students in many classes, allowing information about large groups of students to be obtained efficiently. The risk of this approach is that it does not get information directly from students, and instructor may miss or misinterpret students' difficulties.

Others have used open-ended questions to elicit misconceptions from students (Costu & Ayas, 2005). Boo & Watson (2001) conducted interviews with students each year for three years to monitor their progression in eliminating fundamental misconceptions about chemical reactions. Others have used interviews, as a way to have students make predictions about outcomes of experiments. For example, Shaffer & McBeath (2005) asked people to predict trajectories of fly balls and estimations of the highest points to demonstrate misconceptions about perspective. …

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