Datalogging: a unique affordance unrealized?

Datalogging has the potential to facilitate and extend opportunities for inquiry-based science by providing data and different modalities of representation with minimum effort. The realtime data display provides an immediate link between an experiment and its graphical representation, enabling students to visualize the course of the experiment. It also frees experimentation from time constraints as data can be collected over days, and relieves students from tabulating data and drawing graphs by hand, allowing them to concentrate on the interpretation of data. This paper describes some aspects of a national survey of 593 science teachers on the use of datalogging in Singapore secondary schools (Grades 7-10) and junior colleges (Grades 11-12), interviews of three Science Heads of Department, and classroom observations of datalogging activities. The results suggest that the unique affordances of datalogging are not being fully realised in science learning because teachers generally lack the vision for how dataloggers can be used to enhance the student learning experience in inquiry-based science. Introduction Datalogging methods involve the use of electronic sensors and interfaces to measure and record changes in variables during experiments, variables such as temperature and pH. Data are automatically collected and can be displayed in the form of tables and graphs on a computer screen. This overcomes the time lag between the experiments and the graphs which students would plot manually, and can increase the likelihood of students relating the graphical or diagrammatical representation of relationships to the activity itself (Barton, 1997a; Osborne & Hennessy, 2003); Linn and Hsi (2000) found that when students graphed their own data, they seldom watched the experiment, made many mistakes and lost track of the point of the experiment. A key pedagogical technique used with datalogging, the Predict – Observe – Explain, is also enhanced through the viewing of the representation of the phenomenon on the screen soon after making a prediction (Osborne & Hennessy, 2003). The most recent datalogging software supports sophisticated analysis and transformation of data and graphs. Thus, datalogging can increase students’ participation in the investigation by reducing the “laboriousness of work” (Osborne & Hennessy, 2003, p. 27); it frees students from taking complex measurements, tabulating data, drawing graphs by hand, and executing complex calculations. The benefits of datalogging depend on the quality of students’ thinking about the experiment, for example, asking questions about the experimental design and data, making links with other information, making comparisons and predictions, and looking for trends. It is of little use if the students watch the screen passively and uncritically while the data are being tabulated and graphs plotted, and without actually understanding what is being represented (Newton, 1997; Osborne & Hennessy, 2003; Roger & Wild, 1996). Thus, it is important to “encourage pupils to remain active while the machine is collecting data, using the ‘time bonus’ purposefully to think critically about and discuss the experiment in more depth” (Osborne & Hennesssy, 2003, p. 31). Datalogging also expands the range of questions that can be investigated and the data that can be collected in school contexts by allowing measurements of transient phenomena, as well as monitoring data collection over several hours, days or even weeks (Osborne & Hennessy, 2003; Singer et al., 2000). In sum, it extends learners’ powers of observations and improves the quality of measurement (Rogers & Wild, 1996) © Ministry of Education, Singapore 2005 1 ASERA 2005, 6-9 July 2005, Hamilton, New Zealand Dataloggers were introduced in Singapore schools under the first Information Technology (IT) Masterplan (1997-2002). Secondary schools and junior colleges were given six sets of dataloggers for each laboratory that the school had. Training on the use of dataloggers was mainly provided by the equipment vendors, with the Educational Technology Division, Ministry of Education, Singapore, providing several follow-up workshops. A study of the implementation, efficacy and use of dataloggers in science lessons or project work was initiated in December 2003. This paper describes some of the results obtained from an online survey of science teachers in all secondary schools (Grades 7-10) and junior colleges (Grades 11-12) and interviews with three Science Heads of Department. It includes how dataloggers were used in schools and the opportunities for students to engage in inquiry-based science. The online survey questionnaire The questions in the online survey were designed to collect information from Science Heads of the Department (HODs) and science teachers on their use of dataloggers in science teaching. Two pilot studies involving open-ended questions were conducted, involving a total of 22 teachers from five secondary schools and 11 teachers in professional development graduate programs at the National Institute of Education, Singapore. After the items were finalized, the survey was posted on the website for general access. The questions focused on: 1. whether the Science HODs and teachers had used dataloggers in their lessons, 2. if they had used dataloggers, the subjects and topics in which they used dataloggers, 3. the types of tasks involving dataloggers, 4. the teacher’s role in datalogging activities, 5. the pupil’s role in datalogging activities, 6. how pupils were prepared to use dataloggers, 7. whether pupils were able to interpret data, 8. whether inquiry-based activities were conducted, and if so, how were the inquiry-based activities were conducted, 9. the support structures required for datalogging activities, and 10. the difficulties they faced in conducting datalogging activities. Examples of the items in the online questionnaire are given in the Appendix. The options in the multiple-choice items of the questionnaire were derived from the data collected in the two pilot studies. A majority of the items allowed the respondent to select more than one choice, for example, to capture the various ways the teacher used dataloggers in class. Most items also had an ‘others’ option which allowed the teacher to provide any response which was not included in the options given. The free-response questions allowed the teacher to elaborate on more specific situations and issues, for example, the difficulties faced by students using dataloggers, and how the teacher helped them to overcome any difficulties. In July 2004, letters and emails were sent to the Principals and Science HODs of all secondary school and junior college in Singapore, explaining the objectives and nature of the research project, with a request that six teachers, including the HOD, complete the survey. A total of 175 HODs and 875 science teachers from 175 schools were invited to participate in the online survey. After five weeks, when the online survey was closed, a total of 114 responses from HODs (65.1% of the target) and 479 responses from science © Ministry of Education, Singapore 2005 2 ASERA 2005, 6-9 July 2005, Hamilton, New Zealand teachers (54.7% of the target) were received. The overall response rate to the online survey was 56.5%. Survey results The survey responses showed that 394 respondents (66.4%) had used dataloggers in the past two years (at the time of the survey), 91 (15.3%) last used them more than two years ago, and 108 (18.2%) had not used dataloggers at all. The 394 respondents were thus classified as users of dataloggers, past-users (91), and non-users (108). The users included significantly higher percentages of the two most experienced groups of teachers, those with 16 to 20 years (75.4%) and over 20 years (75.7%) of teaching, than the least experienced teachers, those with less than 5 years of teaching (57.5%). This unexpected finding conflicted with common assumptions about technology use, in that younger teachers were expected to be more adept and more exposed to information technology (Figure 1). Figure 1. Percentage of the different user types in each years of teaching category 0 10 20 30 40 50 60 70 80