You are currently viewing a new version of our website. To view the old version click .
Data
  • Data Descriptor
  • Open Access

22 March 2019

The Importance of Measuring Students’ Opinions and Attitudes

Department of Chemical, Energy, and Mechanical Technology, ESCET. Universidad Rey Juan Carlos, C/Tulipán s/n, 28933 Móstoles, Madrid, Spain

Abstract

The data presented in this article are related to a research carried out at the University Rey Juan Carlos in Spain. Chemical Engineering taught as a subject across three Energy Engineering-based degree streams was evaluated for two academic years. Student insight on course development, their own expectations and results, and the evaluation system were explored via a 33-item survey, receiving 47 full responses. The present contribution provides the full responses obtained from students to the survey administered. The received data were studied applying thorough statistical analyses used to infer conclusions. The full set of data are made public here independently from the research article.
Dataset: Part of the data are presented here and the full responses obtained are deposited in DOI: 10.17632/c7hdf4jpyn.3.
Dataset License: CC-BY

1. Summary

A chemical engineering course was studied. In the assessment system, two midterm exams coexisted with a final exam. After the first academic year covered in the study, only 30% of the students passed the subject, which was considered low. In this context, and taking into account that students learn more effectively with positive perceptions and attitudes towards and activity [1], a modification in the evaluation method of the present course was conceived. The change was made after performing a survey and observing students’ positive perceptions towards a modified assessment method that includes midterms and an exemption on the final exam for students who pass said midterms.
Two cohorts of students were considered in this study (year 1, 2012/13 and year 2, 2013/14). Three degree streams, Energy Engineering (E1), Energy Engineering-Industrial Engineering double degree (E2) and Energy Engineering-Environmental Engineering double degree (E3) were considered. This chemical engineering subject is taken during the second year of the three streams. There are no prerequisites to take this subject.
Once academic year 1 was complete, the survey was sent, via email, to the 99 students who had studied the subject. Fifty-five responses were received, among which eight were incomplete and thus were not considered for further evaluation.
The survey comprised 24 items presented 33 questions grouped in six wider topics. Unless otherwise stated, all items proffered responses on a five-point Likert scale with possible answers ranging from 1 (lowest) to 5 (highest).
The data presented in this article are related to the research article entitled “Students’ performance and perceptions on continuous assessment. Redefining a chemical engineering subject in the European higher education area” [2].
The value of the data lies in three main points:
The complete answers to the survey items can be used by other researchers to confirm the conclusions presented and to compare the statistical parameters obtained with other surveys.
More than 1800 data cells have been deposited in a single Excel file ordered by respondent (rows) and survey item (column), so the file can be used directly by most statistical packages used to treat data.
The open answers given by students can be used to evaluate the analysis content of answers performed and to fully study their feedback.
The exam example and point allocation to six different exams in two academic years are interesting to evaluate the assessment method.

2. Data Description

The continuous evaluation method used in this subject was defined as follows, indicating the points earned out of a total of 100. Pass mark (%) of each item is included if applicable:
  • Practical case 1: mass and energy balances (5 points; no pass mark).
  • Practical case 2: reaction kinetics (5 points; no pass mark).
  • Lab report in groups. Each group of four students attends the labs together and does a joint report on four of the following six experiments (10 points; 40% pass mark): (1) kinetics (ethyl acetate saponification), (2) catalyst preparation, (3) rheology, (4) determination of effective diffusion coefficients, (5) mass balances (Maple software by Maplesoft; Maple 14 version), and (6) energy balances (Maple software).
  • Individual lab exam (10 points; 40% pass mark).
  • Midterm Exam 1: Part 1, lessons 1, 2 and 3 (15 points; no pass mark).
  • Midterm Exam 2: Part 2, lessons 4 and 5 (15 points; no pass mark).
  • Final exam: consisting of two exams analogous to the midterms (20 points each part; 40% pass mark each exam).
Table 1 presents a detailed list of the type of questions included in each exam conducted during years 1 and 2. In addition, the points allocated to each question are also included.
Table 1. Midterm and final examinations performed during years 1 and 2 and detail of points allocated to each topic (out of 10).
An example of an exam is also presented. It was delivered to students as the midterm for the second part of the subject during year 1.
1. (2 points) The diameter of an pipeline suffers a reduction from 5 m to 2 m according to Figure 1. Identify the non-zero components of the tangential stress in the cylindrical coordinate system indicated. Assume that the circulation regime is laminar (1 point).
Figure 1. Diameter reduction in a pipeline.
2. (1 point) In a visit around the Chemical and Energy Technology Department, you stop to observe a reactor used to obtain kinetic data. It consists of a 5 cm diameter glass column into which a silica-based catalyst has been introduced filling it up to a height of 30 cm. The catalyst is packed with a bed porosity (ε) of 0.6. Is this an integral or differential reactor?
3. (2 points) In a batch reactor, a first order reversible reaction A Data 04 00043 i001 R is carried out in the liquid phase. The starting mixture has a concentration of 0.5 mol A/L, with no R. The equilibrium conversion is of 66.7%, and a conversion of 33.3% is reached in 8 min. Obtain the kinetic equation.
4. (2 points) The thermal decomposition of hydrogen iodide to hydrogen and iodine was described by Bodenstein in 1899, with the rate of reaction values being listed in Table 2. Calculate the complete kinetic equation of this reaction taking into account the influence of temperature on the kinetic constant:
Table 2. Rates of reaction at different temperatures for the thermal decomposition of hydrogen iodide.
5. (1.5 points) Nitrogen physisorption data at 77 K have been obtained for three porous materials, obtaining the isotherms presented in Figure 2. Based on the classification established by the IUPAC (Types I to VI), what type of isotherms is each one of them?
Figure 2. Nitrogen adsorption-desorption isotherms at 77 K.
Which of these materials would you use for the following applications? Justify your answer:
(a) Selective adsorption of hydrogen from a hydrogen–butane mixture.
(b) Functionalization by grafting, loading the highest possible number of molecules of an organometallic compound (length of 3 nm) containing nickel.
(c) Immobilization of very large enzyme complexes.
6. (1.5 points) The reaction from S to R is catalysed by an enzyme that presents substrate inhibition. Write the reactions that take place in this process and their reaction rate equations taking into account both equilibrium and irreversible reactions.
The full writing of the survey items is presented in Table 3. The numerical coding used to perform correlation analyses with non-numeric factors (gender, degree, etc.) is shown in Table 4. It has to be emphasized that the main value of the dataset is in questions 23 and 24.
Table 3. Full write-up of items surveyed.
Table 4. Numerical coding of non-numeric variables.
The large amount and comprehensive description of items included in the survey were aimed at obtaining a complete view on the course performance of each student. Thus, when handling the data, it was possible to open up certain discussions while maintaining the anonymity of responses, though it may not look like that for those not well versed on these techniques. Some examples of these discussions are: do students that attend classes respond differently than those who do not attend them? Do students receiving a lower mark than expected have a certain bias when evaluating the aptitudes of the teacher or the difficulty of the exam?
The answers received to item 24 of the survey are presented in Table 5. This item presented an open question and the responses received were used to perform an analysis content in the full paper. The comments were numbered to be consistent with the dataset order; comments by number 10 correspond to the 10th row of data in the dataset. In addition, the complete free comments were collected in Spanish but are presented here and translated to English. Comments have been kept in their original form, so it must be noted that some of them contradict each other or are mistaken regarding several aspects described in the present paper, such as the evaluation method followed, the existence of pass marks, etc.
Table 5. Analysis content of the open-ended answers given to item 24 of the questionnaire.
The conceptual analysis was performed by breaking down the 32 responses to item 24 of the survey, as detailed in Table 5 below. A full description of the frequencies observed is also included. The conclusions drawn are not included here, but in the original research paper [2].
Finally, the full data of the survey have been uploaded to an external repository [3], since it is composed of 1800 cells and thus is too lengthy to be included here.

3. Methods

The questionnaire was administered via email with pre-fixed response options (i.e., an option had to be selected from a drop-down list of answers) for all items except item 1 (age) and item 24 (free opinion). An initial remark including informed consent to the use of the data retrieved for educational purposes was added. Data were acquired by using the online tools provided by e-encuesta.com [4]. Responses were collected anonymously. This point is critical for the ethics on publication and was assured by the software used, which is not owned by the author. Data were also not treated by the software.
The responses to the Likert items of the survey were analysed to determine the frequency of each response. For this purpose, R statistical software [5], and, more specifically, the pre-written code in the ‘jbryer/likert’ package was used [6], with a modification to obtain the number of responses per item.
The correlations among the items were evaluated using a Spearman’s rank correlation coefficient employing R statistical software with the inbuilt ‘sjPlot’ package used to depict the statistics obtained as tiled graphs [7].
The open-ended responses received to item 24 were analysed through a content analysis method. Obtained responses were broken down to single pieces of information containing only one idea and were coded accordingly. The frequency and content of the responses within each code were registered and studied.

Funding

This research received no external funding.

Acknowledgments

The author acknowledges the collaboration of all students who participated in this study. Verónica Sánchez is also warmly thanked for the fruitful discussion of results.

Conflicts of Interest

The author declares no conflict of interest.

References

  1. Marzano, R.J. A Different Kind of Classroom: Teaching with Dimensions of Learning; Association for Supervision and Curriculum Development: Alexandria, VA, USA, 1992; ISBN 0871201925. [Google Scholar]
  2. Sanz-Pérez, E.S. Students’ performance and perceptions on continuous assessment. Redefining a chemical engineering subject in the European higher education area. Educ. Chem. Eng. 2019. [Google Scholar] [CrossRef]
  3. Sanz-Pérez, E.S. Survey on students’ Performance and Perceptions on Continuous Assessment 2018. Available online: https://data.mendeley.com/datasets/c7hdf4jpyn/2 10.17632/c7hdf4jpyn.3 (accessed on 18 March 2019).
  4. Online Surveys. Tool for Creating Surveys. Available online: https://www.e-encuesta.com/ (accessed on 19 November 2018).
  5. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2017. [Google Scholar]
  6. Bryer, J.; Speerschneider, K. jbryer/likert: Analysis and Visualization Likert Items 2017. Available online: https://github.com/jbryer/likert (accessed on 16 August 2017).
  7. Lüdecke, D. sjPlot: Data Visualization for Statistics in Social Science 2017. Available online: https://strengejacke.github.io/sjPlot/ (accessed on 16 August 2017).

Article Metrics

Citations

Article Access Statistics

Multiple requests from the same IP address are counted as one view.