Students' reasoning in choosing measurement instruments in an introductory physics laboratory course

Abstract

The aim of this study is to investigate the decisions and reasoning of undergraduate students when choosing simple measurement instruments in an introductory physics laboratory course. For this study, we have developed a questionnaire and implemented it in a pre-/post-test manner to analyze the influence of lab instruction on both students' decisions and reasoning. To characterize students' justifications, we have inductively developed a coding manual that captures the nuances of students' reasoning when choosing an instrument. It shows that students consider different aspects for their decisions, such as data quality, practical and personal considerations. We have also found that laboratory instruction influenced both students' decisions and justifications, leading to a stronger emphasis on data quality. In fact, after instruction, the majority of students choose the instrument with lower uncertainty and base their justifications mainly on the aim of reducing uncertainties, avoiding systematic effects or mistakes in the instrument reading, and less often than before instruction on personal experience and intuition. These findings suggest that dedicating specific laboratory instruction sessions on measurements and data quality, and having students choose between different instrumentation and provide a justification for their decision, can positively impact students' habits in the laboratory and encourage them to base their choices on evidence rather than intuition.

Figures (4)

• Figure 1: Students' answers to the multiple choice part of the questionnaire. On the $x$-axis are indicated the three possible answers for each item. Top to bottom graphs show respectively items 1 to 4. Pre- and post-test data are indicated with dark and light green. The number $N$ in parenthesis indicates how many students chose each particular answer. The type of instrument (digital or analog) is indicated for each instrument as $[d]$ and $[a]$ respectively.
• Figure 2: Percentage of the students' justifications in the different coding sub-categories (left) and categories (right) considering all items and all kind of instruments. Pre-test data are shown in dark green and post-test data are shown in light green. (Left) On the $x$-axis are reported the coding sub-categories as described in Tab. \ref{['tab:coding_manual']}. (Right) The coding categories indicated on the $x$-axis are, from left to right, data, exp., pers., and unc.. The numbers $N$ indicated in the parenthesis indicates the total number of codes assigned in the pre- and post-test respectively.
• Figure 3: Coding of students' justifications in the categories data, exp., pers., and unc.. Pre-test data are shown in the left graph, post-test results are reported in the right graph. Each item of the questionnaire is indicated with a different color as shown in the figure label. The numbers $N$ indicated in the parenthesis indicates the total number of codes assigned for each item in the pre- and post-test respectively.
• Figure 4: Percentage of students' justifications in the four coding categories as a function of instrument chosen in the multiple choice part of the questionnaire. Top to bottom graphs show respectively items 1 to 4. Results for the pre-test are on the left side of the figure, while post-test results are on the right side of the figure. In the legend, the type of instrument (digital or analog) is indicated for each instrument as $[d]$ and $[a]$ respectively. The percentage here is calculated using the number $N$ of justifications used for each instrument, which is indicated in the parenthesis. Note that the sum of the those percentages for each type of answers sum up to 100%. Instruments with lower uncertainty are indicated with an asterisk.