Automatic Web-Based Grading System: Application in an Advanced

Article pubs.acs.org/jchemeduc

Automatic Web-Based Grading System: Application in an Advanced Instrumental Analysis Chemistry Laboratory Arsenio Muñoz de la Peña,† David González-Gómez,† David Muñoz de la Peña,*,‡ Fabio Gómez-Estern,‡ and Manuel Sánchez Sequedo† †

Department of Analytical Chemistry, University of Extremadura, Avenida de Elvas s/n, 06006, Badajoz, Spain Department of Automation and Systems Engineering, University of Seville, Camino de los Descubrimientos, s/n, 41092, Sevilla, Spain

‡

S Supporting Information *

ABSTRACT: The application of an innovative Web-based system, Goodle GMS, designed for automating the collection and assessment of laboratory exercises is presented. This Web-based system has been extensively used in engineering courses such as control systems, mechanics, and computer programming. Goodle GMS allows the students to submit their results to a server via a Web-based form from any networked computer and receive their grade, feedback, and an indication of where mistakes may have been made. To design new exercises, basic Matlab knowledge is required. As an example, we describe the development and application of Goodle GMS in an advanced analytical chemistry laboratory to determine the nitrite concentration in water samples by using a flow injection analysis (FIA) system, with photometric detection, based on the Griess reaction.

KEYWORDS: Upper-Division Undergraduate, Analytical Chemistry, Laboratory Instruction, Internet Web-Based Learning, Testing/Assessment, Instrumental Methods

T

In mathematics, there are several Web-based systems based on using a computer algebra system (CAS) with Internet submission systems to provide a sophisticated mechanism with which to grade student’s work.8−10 In general, these systems are aimed at designing online courses based on a sequence of short, concise questions that may not be appropriate for general science courses and in particular chemistry courses. A fully automated method of submission, assessment, grading, and commentary for chemical laboratory practical scripts, WebMark, was presented if ref 11. This system is based on designing ad hoc for each exercise, a Web page in which the students provide their answers and a Filemaker Pro script that performs calculations using the student’s data to establish their “right” answers. To this end, the instructor must be proficient not only in a mathematical program such as Matlab, but also in HTML Webpage design, database management, and in the mathematical functions of Filemaker Pro, a proprietary database software, which in general adds some difficulties to the use of the WebMark system. Motivated by these issues, we developed an innovative Webbased education tool named Goodle GMS (grading management system) for automating the collection and assessment of practical exercises of engineering and scientific courses.12−15 In this system, instructors are required to have basic Matlab knowledge in order to design new exercises. The benefits of using this system are the possibility of increasing the periodicity

he European Higher Education System is being overhauled to guarantee more comparable, compatible, and coherent systems in European countries according to the directives established in the Bologna Process.1 In this education framework, instructors are encouraged to provide personalized attention to their students. Thus, tools to minimize instructor grading time will allow them to spend more quality time with students discussing their work. This point is more relevant in courses with a high number of experiments, such as advanced analytical chemistry courses, where the student’s laboratory work constitutes a high percent of their class time. Education institutions and universities have started using collaborative Web tools such as Moodle or Blackboard to allow instructors and students to benefit from the advantages they offer in the learning process. An overview of what can be done in a course using the Web is presented in refs 2−4 and an assessment of learning management systems (LMS) usage in first-year chemistry courses is found in ref 5. LMS are effective in distributing material and collecting student assignments; however, they offer limited evaluation functionalities. In general, most LMS only offer the possibility of evaluating multiple-choice questions (MCQs); for examples of applications in chemistry courses see refs 5−7. Although MCQs are a useful tool, they are not appropriate for the assessment of tasks requiring numerical manipulation, calculations, and complex logical processes that bring together conclusions from different sets of data, such as practical exercises developed in a laboratory. © 2013 American Chemical Society and Division of Chemical Education, Inc.

Published: January 24, 2013 308

dx.doi.org/10.1021/ed3000815 | J. Chem. Educ. 2013, 90, 308−314

Journal of Chemical Education

Article

Figure 1. System network architecture.

follow the instructions provided in the exercise wording. Goodle GMS then makes a code-fusion operation: the studentprovided code is merged with code developed by the instructor. In order to grade the results, the system executes the resulting program. The code developed by the instructor (the evaluation code) compares the variables generated by the student part with the correct solutions of the problem. According to this comparison, the evaluation code provides a grade, feedback, and an indication of where mistakes may have been made which are stored in the database. The workflow for both types of users and their interactions with the system are shown in Figure 2.

of exams and exercises, total objectivity in checking and grading, and reducing the time spent in time-intensive, repetitive assessment: see ref 11 for a discussion of these issues in the chemistry field. Goodle GMS has been applied in several programming and automatic control courses; however, it has not been used in other knowledge areas such as chemistry, and moreover, it has never been used to evaluate laboratory exercise results. Here we use Goodle GMS to grade experimental exercises in which the results of students depend on their laboratory work. Goodle GMS was used to grade the experimental practice of four analytical chemistry courses from 2010 to 2012. Over 70 students used the Web-based system to submit the results of the determination of nitrite in water samples by using a flow injection analysis (FIA) system, with photometric detection, based on the Griess reaction.

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GOODLE GRADING MANAGEMENT SYSTEM Goodle GMS is a Web application that is executed on a dedicated server.12−15 Both the students and the instructors access the server using a standard Web browser. In Figure 1 (left part), the network architecture of the system is illustrated. The students can be located at the facilities of the university or outside. In Figure 1 (right part), the workflow of the evaluation system is shown. Most virtual education platforms often include automatic evaluation tools. The most common tools are multiple-choicequestion tests where the student must answer some question by choosing one or more options from a list of available answers.5−8 Goodle GMS is based on a different evaluation method. The key idea is that the evaluation process is open and programmable. To make this possible, the students must submit their results following a given programming language syntax (Matlab among other possibilities). The students may not be aware of this requirement and in general do not need to know the appropriate syntax rules. The students only need to

Figure 2. Workflow for both types of users and their interaction with the system.

Using the proposed approach, it is possible to assess complex logical processes that bring together conclusions from different sets of data and also to personalize the numerical parameters of the exercises as a function of the digits of a personal unique identification (ID) or enrolment number (that may be provided by the instructor). In that case, each student is given a unique problem to solve and they will be unable to share their solutions. This architecture requires an exchange between the database, containing the student ID number, and the evaluation 309



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where yi represents the value of the instrumental signal obtained for a concentration xi of standard analyte, and x̅ and y ̅ are the mean values of the set of concentrations and signals used in the calibration. The standard deviation of the regression of y over x, sy/x, is calculated as follows:

code. To submit results, the student must log in using a security password field, which allows for a correct handling of the data. A detailed description of Goodle GMS can be found in ref 15.

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NITRITE DETERMINATION IN NATURAL WATERS BY FLOW INJECTION ANALYSIS LABORATORY EXERCISE The Goodle GMS system was used to assess student’s results of a nitrite determination in natural waters by flow injection analysis laboratory exercise. This laboratory exercise was carried out by over 70 students of four different advanced analytical chemistry courses from 2010 to 2012. First, the students followed a 4 h laboratory class to obtain raw data for the nitrite determination. Afterward, the students completed the calculations and interpreted the raw data using ACOC.16,17 ACOC is a software package for statistical analysis by linearregression written for the Matlab programming environment, intended to perform the necessary calculations for the linear adjustment of the experimental data of instrumental analytical chemistry laboratories. The results of the lab experiment were uploaded to the grading system by the students. The experience was complemented with a survey to evaluate the student satisfaction with the proposed methodology.

Sy / x =

Sx =

n

(3)

Sy / x ⎛ 1 (y − y ̅ )2 1⎞ ⎜ + ⎟+ 2 n m ⎝M n⎠ m ∑i = 1 (xi − x ̅ )2

(5)

LABORATORY EXPERIMENT DESCRIPTION The determination of nitrite in water samples by means of the Griess reaction using flow-injection analysis with photometric detection is examined.19 The nitrite ion is colorless and slightly basic and can act as oxidizer or reducer. In acid medium, this ion is found as HNO2, being an unstable substance, especially if the temperature is elevated. In those conditions, the NO2− anion is transformed to NO3− and NO. Nitrites are widely employed in the manufacture of colorant agents, drugs, and many organic chemical products, as well as in food industry such as meat preservatives. It is also present in natural waters in variable concentrations, and in this matrix, it is determined by students. Diverse methods have been employed for its analysis, with spectrophotometric methods the most frequent. This method is based in the reaction of sulfanilamide with nitrite in acidic medium to generate a diazocompound, which reacts with an aromatic amine, N-[1-naphthyl]ethylenediamine, to produce a colored compound, with an absorption maximum at λ = 540 nm, where the spectrophotometric measurement is performed. The reaction of sulfanilamide with nitrite in an acidic medium is shown in Scheme 1. Flow-injection analysis (FIA) involves the injection of a liquid sample into a moving nonsegmented continuous carrier stream of a suitable liquid. The injected sample forms a zone, which is then transported toward a detector that continuously records the analytical signal as it continuously changes, due to the passage of the sample material through the flow cell. A general scheme of the FIA system used is shown in Figure 3. In the manifold, two channels are necessary for the reactions to occur. First, one channel is aspirating the sulfanilamide and it reacts with the injected nitrite sample; then, through a second channel, N-[1-naphthyl]ethylenediamine is aspirated and the second reaction is performed. In this experiment, students have to quantify the presence of nitrites in natural waters. The presence of nitrites in waters implies contamination, as nitrites are likely to cause different diseases in healthy people.19 Students prepare a solution of sulfanilamide 10 g/L in HCl 0.7 M (reagent 1), a solution of N-[1-naphthyl]ethylenediamine

where y is the analytical signal, x is the concentration, and b and m are the intercept and slope of the regression line, respectively. This relation is obtained using the statistical model of a linear regression (least-squares). This statistical model finds the best linear fit between the experimental data and the concentration and gives a measure of the precision of the calculations. The regression standard deviation and the standard deviation of the slope and intercept can be also determined. The slope and intercept values are calculated according to

b = y ̅ − mx ̅

(4)

■

(1)

(2)

i=1

where M is the number of repetitions of the analysis of the problem sample and y is the arithmetic mean of the signal values obtained from the M analysis.

THEORY OF THE LABORATORY EXPERIMENT In many laboratory experiments on instrumental analysis, students need to perform a linear-regression fit of an instrumental signal and a chemistry-controlled variable (usually the concentration of an unknown in a problem sample). The use of an unweighted linear regression for the establishment of a linear calibration curve involves the acceptance of four assumptions:18 • The y axis contains the instrumental response and the x axis the concentration. • The errors must occur only in the instrumental signal measurements. • The errors must be normally distributed. • The absolute errors on the instrumental signal measurements are constant and independent from the concentration of the standard solutions (homocedasticity). The usual procedure to build a calibration curve is as follows. A set of standard samples is prepared. In these samples, the concentration of the analyte must be known. This set of samples constitutes the calibration standards, which are measured in the analytical instrument under the same conditions. For each standard there is an analytical signal. The next step is to fit these results to a linear equation

∑ [(xi − x ̅ )(yi − y ̅ )] m = i=1 n ∑i = 1 (xi − x ̅ )2

n

∑ (yi − yi ̂ )2

where ŷi is the value of signal calculated from the regression equation, corresponding to an xi concentration, and n is the total number of points used to calculate the regression line; sy/x is a measure of the goodness of the fit of the experimental data to a line. In the prediction of an unknown in the problem sample using the external calibration method, the general expression to calculate the standard deviation of the prediction, in concentration units, is given by

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y = mx + b

1 n−2

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Scheme 1. Reaction of Sulfanilamide with Nitrite in an Acidic Medium

Figure 3. Experimental setup.

1g/L (reagent 2), and a stock solution of nitrite 20.0 μg/mL. From this stock solution, five standards are prepared (0.5, 1.0, 2.0, 3.0, and 4.0 μg/mL). Three replicates are needed for each standard, and the total number of experimental points is 15. The unknown nitrite in the problem sample needs to be diluted before analysis to have a nitrite concentration that gives an instrumental response in the interval of the linear calibration performed. Note that a dilution step is usually performed in any instrumental analysis laboratory for the above related reasons. Using the FIA manifold indicated (Figure 3), the corresponding injections of the standards were realized. With the same procedure, three measurements of the unknown problem sample were realized, obtaining three replicated peaks. The concentration of nitrite in the problem sample can be calculated by substituting the mean values of the signals of the unknown in the calibration line, constructed by the students.

calculations for the determination of the nitrite concentration in the problem sample. An example of the FIAgram obtained corresponding to the standards and to a problem sample, presenting the absorbance peaks measured at 540 nm by the spectrophotometric detector, is shown in Figure 4. For the

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STUDENT LAB WORK The student lab work was organized in two stages; the first part was carried out in the analytical chemistry lab, where the students performed the different experiments. Each student was provided with a lab handout that described the experimental procedure. Then, they prepared in the lab each of the necessary reagent solutions to realize the experiment, as well as the calibration step in the determination of the content of nitrites of the problem sample. At the end of the experimental phase, all students had recorded the necessary raw data to perform the

Figure 4. FIAgram corresponding to the standards and to the problem sample.

construction of the calibration curves, the height of the absorbance peaks was used as the analytical signal. 311



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finally, 2 for errors higher than 15%. This grade was modified subtracting 1 point if the result or the associated uncertainties were not expressed correctly. Each student had a different problem sample, containing from 12 to 18 μg/mL nitrite concentration. Note that the grading criteria can be easily modified by the instructor changing the evaluation code. The evaluation code obtained the corresponding concentration based on the ID of the student, which is a variable present in the workspace. The evaluation code not only provides a grade, but may also provide students a detailed report of their results. In the Supporting Information, the evaluation code used for this exercise is available. Developing the Matlab code to automatically evaluate the student’s results for a particular problem requires an initial effort; however, one of the advantages of using the proposed approach is the possibility of using in different courses the same evaluation code. Code reusability is one the cornerstones of computer science and software engineering and can justify the effort of the instructor in developing the evaluation code. In addition, it is important to note that the automatic evaluation approach can be used as a grading aid; that is, it can be used to improve the traditional individual grading procedure by analyzing the raw data, saving a lot of time of the instructor, and moreover, providing useful information to assess the progress of students.11 Note that the grades obtained automatically are precise and objective, being an adequate tool to evaluate a high number of students under the same criteria. In addition, the developed code is of general purpose and not only useful for the particular laboratory practice described in the example developed in the article, as it can be used for any instrumental laboratory practice in which an unweighted univariate calibration is necessary. For that, the instructor only has to change the information about the personalized concentration problem of each student, which is one of the variables input needed, and the dilution performed, on the original problem sample. In that manner, the code serves as a generalized evaluation code, useful for grading student results in any instrumental analysis laboratory.

The second stage of the practice was carried out in the computer lab, where the students used the ACOC software to complete the calculations for the determination of the problem nitrite concentration from the raw data obtained in the laboratory. The results, both the raw data and the unknown problem nitrite concentration, where uploaded to Goodle GMS. To this end, each student registered on the server with his or her identification number and a password. The students uploaded the dilution factor they used, which is a variable that the code need to perform the correct calculation of the nitrite concentration in the problem sample, the 15 pairs of experimental values (xi, yij), corresponding to the 15 solutions of the standards (5 standards with 3 replicates each), the responses of the replicates of the problem sample (y1, y2, y3) and, finally, the calculated concentration of nitrites in the problem sample (xp) as well as the associated uncertainty (ep). To evaluate the concentration and the uncertainty, the students had to take into account the dilution. The uncertainty is given by tsx, for a Student t value corresponding to n − 2 degrees of freedom and a significance level of 95%; that is, in this particular case, 2.16. Students submitted their laboratory experiment results via the Goodle GMS interface using the code structure shown in Box 1. Box 1. Code structure of the students’ laboratory results submissions Dilution = d; Standards = [x1 y11 y12 y13; x2 y21 y22 y23; x3 y31 y32 y33; x4 y41 y42 y43; x5 y51 y52 y53]; Replicates = [y1 y2 y3]; Concentration = [xp ep]; The term d is the dilution performed by the student to solve its particular problem. The term x1 is the x value of the first concentration standard, while the term y11 is the y value (analytical signal) of the first replicate of the first standard, and so on. The terms y1, y2, and y3 are the first, second, and third replicate of the problem, respectively. To facilitate the uploading of the answers to the students, the server provides a template with an adequate format, as shown in Figure 5.

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DISCUSSION The proposed system was used in a single experiment with a course of 22 students during 2010−2011. Motivated by the good results obtained in the previous course, the system was applied during 2011−2012 with 52 students in two different undergraduate courses and in one master course. The instructors involved in the experience have a very positive impression of the overall results and that they plan to keep working on developing new exercises for future courses. Thanks to the built-in syntax filter and other Goodle GMS safeguards, the process of uploading the results, as well as the evaluation process, was completed without any problems. Note that if there are typographical errors, the solution handed can be corrected and evaluated again (one of the advantages of using this system). Nevertheless, the students should learn to be precise and avoid this class of errors, and the automatic evaluation is a good test for these skills. With respect to the possibility of student manipulation of the system, this is a problem which is highly dependent on the particular exercise/setting. In the case studied, the students were asked to provide the system with the experimental data in the exercise solution. To avoid manipulation, this information could be gathered by the instructor (or uploaded) during the laboratory exercise; that is, just after the experiments are carried out. Note, however, that raw data manipulation could also take

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EVALUATION CODE DESIGN The results submitted by the students in text form can be executed as Matlab code to generate in the workspace a set of variables with the dilution (Dilution), raw data (Standards and Replicates), and the nitrite concentration in the problem sample (Concentration). The server executes this code and then the evaluation code designed by the instructor for the exercise. The evaluation code makes the calculations using ACOC16,17 and the student’s raw data to establish their “right” answers. The student’s concentration is then compared with the right answers to generate a grade and some comments in text form. In this particular exercise, the grading criteria were established taking into account the precision of the determination of the problem sample, the calculations, and the correct expression of the uncertainty associated to the results. If the error was below 1%, the students grade was 10 (the maximum grade); if the error was between 1 and 2%, the grade was 9; 8 for errors between 2 and 5%; 6 for errors between 5 and 10%; 4 for errors between 10 and 15%; and, 312



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Figure 5. Goodle GMS results submission page.

acquire skills in the competent recording of laboratory data and the writing of good quality scientific reports.

place if the students submit the results by hand and is not a problem inherent to the proposed scheme. At the end of each laboratory session, a survey was given to the students to assess their degree of acceptance of the automatic evaluation system used. Table 1 shows the results of

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Table 1. Comparative Survey Results Concerning Students’ Use of Goodle GMS Students’ Responses (%)a,b Items for Response (Topics)

SA

A

D

SD

Q1 (Use of Web page to submit answers to practical exercises) Q2 (Use of server to grade students’ practical exercises) Q3 (Ease of use of Goodle GMS) Q4 (Automatic evaluation improves learning as it can provide continuous assessment) Q5 (Satisfaction with the practices of the course)

4

52

35

9

17

63

14

6

31 15

45 62

14 18

10 5

33

61

6

0

CONCLUSIONS

A tool for the automatic correction and grading of laboratory practices of an advanced analytical chemistry course has been presented. The application has been tested with a group of over 70 students during 2010−2012 with satisfactory results. The proposed system has its own limitations and drawbacks that the instructors must be aware of. Google GMS does not aim at replacing traditional assessment methods, but rather at complementing them. Designing an exercise, grading, and providing comments to the students are time-consuming tasks, so in general, there are limits on the amount of feedback a teacher can provide to his or her class. The use of Goodle GMS in a course of instrumental analysis may allow the teacher to focus on the theoretical issues, while still assessing the more technical abilities of his students, for example, to correctly process the experimental data obtained. We have presented the system and a particular application in which the students received the grade and some comments. There are other uses that go beyond the scope of the article, for example, using the proposed system to provide the grades during the experiment, providing the students with instantaneous feedback on their advances, so they can correct the experiments. Goodle GMS is a tool that can be used to improve the teaching of an analytical chemistry course by providing a precise evaluation of the work developed in laboratory exercises. In addition, the results of this experience demonstrated that Goodle GMS can be used in courses outside the automatic control and computer sciences, in particular in any of the scientific or technical areas, in which numerical results need to be graded. It is also important to note that this is the first time that Goodle GMS has been used to analyze experimental data obtained in a laboratory practice. Should the reader be interested in testing and using the application by freely creating auto-evaluated exercises (or using the

a

SA: Strongly agree; A: Agree; D: Disagree; SD: Strongly disagree. b N = 74.

the survey. With respect to Goodle GMS, the survey shows that in general the students find the system easy to use and that they accept the use of information and communication technologies to collect and grade exercises. Similar acceptance results were obtained in a survey performed to 334 students in the autumn quarter of 2008, in the Course Automatic Control, a third year compulsory course of the Industrial Engineer degree of the University of Sevilla.15 The instructors found that the students did not have any problems using the application and that their main issues were related with a possible increase in the workload of the course because of the use of Goodle GMS to assess exercises. It is important to remark that Goodle GMS is not proposed as a method for assessing all laboratory reports, but rather as a method for efficiently assessing those reports with a high proportion of quantitative answers. We recognize the need for students to 313



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(13) Gómez-Estern, F.; Muñoz de la Peña, D. A new Web-based tool for education and automatic evaluation in Control Systems Engineering, 8th IFAC Symposium on Advances in Control Education, Kumamoto, Japan, 2009. (14) López-Martínez, M.; Gómez-Estern, F.; Muñoz de la Peña, D. Int. J. Knowl., Sci. Technol. 2010, 1, 1−6. (15) Muñoz de la Peña, D.; Gómez-Estern, F.; Dormido, S. Comput. Educ. 2012, 59, 535−550. (16) Espinosa Mansilla, A.; Muñoz de la Peña, A.; González Gómez, D. Chem. Educator 2005, 10, 337−345. (17) Galeano Díaz, T.; Muñoz de la Peña, A.; Espinosa Mansilla, A.; Durán Martín Merás, I.; Acedo Valenzuela, M. I.; Cañada Cañada, F.; ́ ́ para Quimica González Gómez, D. ACOC v2.0, Herramienta Estadistica ́ Analitica; Servicio de Publicaciones de la Universidad de Extremadura: Cáceres, Spain, 2006. (18) Cuadros Rodríguez, L.; García Campaña, A. M.; Jiménez Linares, C.; Román Ceba, M. Anal. Lett. 1993, 26, 1243−1258. (19) Muñoz de la Peña, A.; Cañada, F.; Airado, D. Práctica de ́ ́ Laboratorio de Quimica Analitica Avanzada: Determinación de Nitritos en Aguas Naturales Mediante Análisis por Inyección en Flujo (FIA): Servicio de Publicaciones de la Universidad de Extremadura: Cáceres, Spain, 2007.

code presented in this work) and assigning them to their students, it is possible to do so on a fully functional application running on a publicly available server. Please contact the authors by email to gain password access.

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ASSOCIATED CONTENT

S Supporting Information *

The evaluation code developed for the nitrite determination in natural waters by flow injection analysis laboratory exercise. This material is available via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The financial support of Vicerrectorado de Calidad e Infraestructura de la Universidad de Extremadura (Convocatoria de Ayudas para la mejora de la calidad docente Cursos 2011-2012 and 2012-2013), Ministerio de Economı ́a y Competitividad of Spain (Project CTQ2011-25388) and Gobierno de Extremadura (Consolidation Project GR-10033 Research Group FQM003), both cofinanced by European FEDER funds, is acknowledged. D.M.P. and F.G. acknowledge the financial support of the University of Seville, under ́ Docentes, the programme Plan de Renovación de las Metodologias 2007-2010.

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REFERENCES

(1) van der Wende, M. C. The Bologna Declaration: Enhancing the Transparency and Competitiveness of European Higher Education. Higher Educ. Eur. 2000, 25, 305−310. (2) Poindexter, S. E.; Heck, B. S IEEE Control Syst. Mag. 1999, 19, 83−92. (3) Kapur, S.; Stillman, G. Innovations in Educ. Training Int. 1997, 34, 316−322. (4) Heck, B. S.; Poindexter, S. E.; R. Garcia, R. Proc. Am. Control Conf. 2000, 5, 3444−3448. (5) Charlesworth, P.; Vician, C. Leveraging Technology for Chemical Sciences Education: An Early Assessment of WebCT Usage in FirstYear Chemistry Courses. J. Chem. Educ. 2003, 80, 1333−1337. (6) Penn, J. H.; Nedeff, V. M.; Gozdzik, G. Organic Chemistry and the Internet: A Web-Based Approach to Homework and Testing Using the WE_LEARN System. J. Chem. Educ. 2000, 77, 227−231. (7) Stewart, B.; Kirk, R.; LaBrecque, D.; Amar, F. G.; Bruce, M. R. M. InterChemNet: Integrating Instrumentation, Management, and Assessment in the General Chemistry Laboratory Course. J. Chem. Educ. 2006, 83, 494−500. (8) Klai, S.; Kolokolnikov, T.; van den Bergh, N. Using Maple and the Web To Grade Mathematics Tests. Int. Workshop Adv. Learn. Technol. (IWALT 2000) 2000, 89−92. (9) Sangwin, C. J. Teach. Math. Appl. 2004, 23, 1−14. (10) Mavrikis, M.; Maciocia, A. WaLLiS: a Web-based ILE for science and engineering students studying mathematics. In Workshop of Advanced Technologies for Mathematics Education in 11th International Conference on Artificial Intelligence in Education, Sydney, Australia, 2003. (11) Olivier, G. W. J.; Herson, K.; Sosabowski, M. H. WebMarkA Fully Automated Method of Submission, Assessment, Grading, and Commentary for Laboratory Practical Scripts. J. Chem. Educ. 2001, 78, 1699−1703. (12) Gómez-Estern, F.; López-Martínez, M.; Muñoz de la Peña, D. Rev. Iberoam. Automática Inf. Ind. 2010, 3, 111−119. 314


Automatic Web-Based Grading System: Application in an Advanced

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