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Automated Assessment in Programming Courses:
A Case Study during the COVID-19 Era
Enrique Barra 1,* , Sonsoles López-Pernas 1 , Álvaro Alonso 1 ,
Juan Fernando Sánchez-Rada 1 , Aldo Gordillo 2 and Juan Quemada 1
1 Departamento de Ingeniería de Sistemas Telemáticos, Escuela Técnica Superior de Ingenieros de
Telecomunicación, Universidad Politécnica de Madrid, 28040 Madrid, Spain;
sonsoles.lopez.pernas@upm.es (S.L.-P.); alvaro.alonso@upm.es (Á.A.); jf.sanchez@upm.es (J.F.S.-R.);
juan.quemada@upm.es (J.Q.)
2 Departamento de Sistemas Informáticos, Escuela Técnica Superior de Ingenieros de Sistemas Informáticos,
Universidad Politécnica de Madrid, 28031 Madrid, Spain; a.gordillo@upm.es
* Correspondence: enrique.barra@upm.es
Received: 5 August 2020; Accepted: 7 September 2020; Published: 10 September 2020Abstract: The COVID-19 pandemic imposed in many countries, in the short term, the interruption of
face-to-face teaching activities and, in the medium term, the existence of a ‘new normal’, in which
teaching methods should be able to switch from face-to-face to remote overnight. However,
this flexibility can pose a great difficulty, especially in the assessment of practical courses with a high
student–teacher ratio, in which the assessment tools or methods used in face-to-face learning are not
ready to be adopted within a fully online environment. This article presents a case study describing
the transformation of the assessment method of a programming course in higher education to a
fully online format during the COVID-19 pandemic, by means of an automated student-centered
assessment tool. To evaluate the new assessment method, we studied students’ interactions with the
tool, as well as students’ perceptions, which were measured with two different surveys: one for the
programming assignments and one for the final exam. The results show that the students’ perceptions
of the assessment tool were highly positive: if using the tool had been optional, the majority of them
would have chosen to use it without a doubt, and they would like other courses to involve a tool like
the one presented in this article. A discussion about the use of this tool in subsequent years in the same
and related courses is also presented, analyzing the sustainability of this new assessment method.
Keywords: assessment; assessment process; assessment tools; e-learning; assessment techniques;
automated assessment; online education; computer science education
1. Introduction
The global pandemic of COVID-19 led to the suspension of face-to-face teaching activities in many
countries. In the higher education context, the abrupt transformation of classroom teaching into an
online format was carried out practically overnight with the aid of tools such as videoconferencing
software for synchronous activities or lecture recording programs for the creation of videos that can be
shared with students through learning management systems (LMSs). However, the evaluation process
of some courses could not be easily transformed to an online format, since assessing the attainment of
the course learning objectives is often a complex procedure that supports the whole process of teaching
and learning [1]. This evaluation is the result of the previous adaptation of the courses to the European
Higher Education Area (EHEA) guidelines and principles [2] aligned with the national agencies that
generate the procedures, recommendations, guidelines, and support documents to implement those
recommendations, such as ANECA (National Agency for Quality Assessment and Accreditation,
Sustainability 2020, 12, 7451; doi:10.3390/su12187451 www.mdpi.com/journal/sustainability
Sustainability 2020, 12, 7451 2 of 24
in its Spanish acronym) in Spain [3]. The finalist nature of the evaluation process was abandoned in
favor of a new learning-oriented approach, in which feedback—which contributes to the continuous
improvement of learning—gains prominence [4–6].
Carless et al. [7] established a conceptual framework for learning-oriented assessment. Carless [8]
developed the concept of learning-oriented assessment itself through three characteristic elements:
(1) assessment tasks should be designed to stimulate sound learning practices among students;
(2) the involvement of students in the assessment process, as exemplified by the development of
evaluative skills; and (3) timely feedback which feeds forward by prompting student engagement
and action. This learning-oriented assessment, with its three main characteristic elements, is usually
implemented in programming courses in the form of several programming assignments, along with
a final face-to-face written exam in which students have to answer several theoretical and practical
questions, although there is often a mid-term face-to-face exam as well [9,10]. An important distinction
to highlight is that the programming assignments are used for formative assessment, and the written
exams are used for summative assessment. Taras [11] analyzed both types of assessment and their
characteristics, and concluded that formative assessment is in fact summative assessment combined
with feedback that can be used by the learner. Although this is the main difference between summative
and formative assessment, other authors—such as Harlen and James [12]—deeply characterize both
types and enumerate many other differences to conclude that, although they have separate functions,
they can be used together, complementing each other. Carless states that learning-oriented assessment
can be achieved through either formative or summative assessment, as long as the central focus is on
engineering appropriate student learning [8].
With the cancellation of classes due to COVID-19, in Spain, the Network of University Quality
Agencies (REACU, in its Spanish acronym), together with ANECA, made public an agreement in
which universities were requested to adopt evaluation methodologies that made the best possible
use of the resources at their disposal, aligning themselves with the quality standards in force in the
European Higher Education Area (EHEA), so that the following general criteria were met [13,14]:
(a)
the use of different assessment methods, based on continuous assessment techniques and
individual tests;
(b) these methods must enable the evaluation of the acquisition of the competences and learning
outcomes of the subjects;
(c)
the criteria and methods of evaluation, as well as the criteria for grading, should be made public
well in advance and included in the subject teaching guides as addenda;
In the context of programming courses, meeting the aforementioned criteria required programming
assignments and exams to be transformed to a fully online format. Programming assignments present
several benefits for teachers and students. For instance, they can help transfer theoretical knowledge
into practical programming skills and enhance student programming skills [10]. To fully realize these
benefits, assessment systems for programming assignments should be used in order to grade the
assignments and provide feedback to the students. These systems can be classified in the first place by
assessment type. The first type is manual systems, such as the system described in [15], which assists
the instructor in assessing students’ assignments, but the assessment itself is performed manually by
the instructor. Then, there are automated assessment systems (for example [16], which assess students’
solutions automatically). Finally, semi-automated systems, such as [17], assess students’ assignments
automatically, but also require that the instructor perform additional manual inspections. Automated
assessment systems can be also classified according to the strategy by which the assessment process is
triggered. They can be student-centered, instructor-centered, or hybrid, the latter being a strategy aimed
at exploring the strengths of both instructor-centered and student-centered approaches [18]. Overall,
automated assessment systems have the potential to facilitate the transformation of programming
assignments into an online format, especially in scenarios with a high student–teacher ratio, with the
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support of other tools within a course LMS, such as the forum, notifications, or direct messages among
teachers and students.
The face-to-face written exams also had to be transformed into an online format with the help of
the available tools that each higher institution provided, although, in many cases, these were limited to
what the LMS allowed, due to the urgency of the transformation and the lack of time to acquire new
tools and train the teachers on how to use them.
Being a central part of the learning process, assessment is an important influence on students’
learning and how they approach it. Entwistle [19] found that students’ perception of the learning
environment determines how they learn, and not necessarily the educational context in itself. According
to Struyven, Dochy and Janssens [20], students approach learning differently depending on their
perceptions of the evaluation and assessment, varying among a deep approach (an active conceptual
analysis that generally results in a deep level of understanding), a surface approach (an intention
to complete the learning task with little personal engagement, often associated with routine and
unreflective memorization), and a strategic or achieving approach (an intention to achieve the
highest possible grades by using well-organized and conscientious study methods and effective
time management).
The recent COVID-19 pandemic was very sudden, and led to a paradigm shift in teaching and
learning. Agencies have reacted quickly, creating generic recommendations and guidelines such as the
ones mentioned earlier, but further research is needed to successfully and efficiently adapt specific
courses to the new requirements. Addressing this research gap is especially urgent in contexts and
scenarios in which the adaptation is not straightforward, such as in courses with high student–teacher
ratios or in practical courses where the assessment tools or methods used in face-to-face learning are
not ready to be adopted within a fully online environment.
This article presents a case study describing the transformation of the assessment method of a
programming course in higher education to a fully online format during the COVID-19 pandemic by
means of an automated student-centered assessment tool. To evaluate the new assessment method,
we studied students’ interactions with the tool, as well as students’ perceptions (meaning “the way that
someone thinks and feels about a company, product, service, etc.” [21]), measured with two different
surveys: one for programming assignments and one for the final exam. The results obtained have
allowed us to analyze the sustainability of the newly developed assessment method and how it should
be improved for future editions of the same course and related ones, thus filling the research gap
identified earlier.
The rest of the article is organized as follows. Existing literature on automated assessment systems
is reviewed in the next section. Section 3 explains the student assessment method followed in the case
study presented, and its evaluation. Then, Section 4 shows and discusses the results obtained from
said evaluation. Lastly, Section 5 finishes with the conclusions of the article with an outlook on future
work, and Section 6 presents the limitations of the case study.
2. Related Work
Quite a number of literature reviews on automated assessment systems for programming
assignments have been published over the past years [18,22–29]. These literature reviews have
classified these systems according to different aspects, such as epoch [22], assessment type (automated,
semi-automated, or manual) [18], analysis approach (static, dynamic, or hybrid) [27,28], assessment
process triggering (student-centered, instructor-centered, or hybrid) [18], and purpose (competitions,
quizzes, software testing, or non-specialized) [18]. Moreover, these literature reviews have analyzed the
features offered by automated assessment systems [18,23–27]. In this regard, it is worth pointing out
that, generally, these systems provide electronic submission, automated and often immediate feedback,
automated grading, and statistics reporting. Pieterse [29] investigated the factors that contribute to the
successful application of automated assessment systems for programming assignments, concluding that
these factors include the quality and clarity of the assignments, well-chosen test data, useful feedback,
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the testing maturity of students, the possibility of performing unlimited submissions, and additional
support. In total, more than 100 automated assessment systems for programming assignments have been
reported in the literature. Among the most popular of these systems are Mooshak [30], DOMjudge [31],
CourseMarker [9], BOSS [32], WebWork [33], and Automata [34]. Automated assessment systems for
programming assignments help teachers to evaluate programs written by students, and provide them
with timely feedback [35]. One of the main reasons for introducing these systems in programming
courses is their capacity to dramatically reduce teachers’ workloads. In this regard, Bai [36] shows that
these systems allow teachers to save time and, at the same time, provide quicker feedback and deliver
more assignments. It must also be mentioned that these systems require a more careful pedagogical
design of the student programming assignments on the part of the instructors [22,23,29,37], and that
they can change how students approach these assignments [24].
Automated assessment systems are usually classified as formative assessment tools, as the
feedback these tools provide usually consists of “information communicated to the learner with the
intention to modify his or her thinking or behavior for the purpose of improving learning” [38],
but sometimes, these tools can be considered as summative assessment tools if they only provide the
grades or percentages. Most of the time, the feedback provided is a configuration option that the
instructor has to provide when creating the assignments. Keuning et al. [26] conducted a systematic
review of automated assessment tools with a special focus on the feedback generated.
Regarding the reliability and validity of these systems, they have been studied comparing
manually graded assignments with the system generated grades. Gaudencio, Dantas and Guerrero [39]
reported that instructors who manually graded assignments tended to agree more often with the
grades calculated by an automated assessment system (75–97%) than with the ones provided by other
instructors (62–95%). Moreover, a number of authors [9,40,41] have also reported on the grading
consistency rates between automated systems and instructors, highlighting the reliability and lack of
subjectivity that these systems present.
In addition to the benefits that automated assessment systems can provide for teachers, these systems
can yield important benefits for students as well. Several works have evaluated the use of automated
assessment systems for student programming assignments in the context of programming courses,
concluding that this kind of system is capable of producing positive effects on both students’
perceptions [37,42–48] and performance [47–49]. However, experiences have also been reported in which
the use of these systems did not produce significant positive results. For example, Rubio-Sánchez et al. [50]
evaluated Mooshak, and concluded that the generated feedback needs to be richer in order to improve
student acceptance, and that there was no evidence to claim that its use helped to decrease the dropout rate.
In this regard, it should be taken into account that the effectiveness of an automated assessment system in
a programming course ultimately relies on how it is used and integrated into the course [29]. Therefore,
it becomes clear that the teaching methodology adopted in a programming course plays a crucial role in
the successful application of an automated assessment system. Evidence of this fact is that the same
automated assessment system can succeed in reducing the dropout rates in a programming course [49],
but fail to do so in another programming course with a different teaching methodology [50]. Although
examining the effect of combining automated assessment systems for programming assignments with
different teaching methodologies would be a valuable contribution, no research work has addressed
this research issue yet. Moreover, no study has yet examined the use of these systems for providing
student assessment methods in programming courses following the teaching methodologies adopted in
response to the COVID-19 pandemic.
3. Description of the Case Study
The case study presented in this article follows the research design described by Yin [51]. Yin defines
a case study as “an empirical inquiry that investigates a contemporary phenomenon (the ‘case’) in
depth and within its real-world context”. The theoretical framework of this study was introduced in
the first two sections of this article. It is based on educational assessment, specifically learning-oriented
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assessment (either summative or formative), and the automated assessment tools. The research gap
identified was also stated as how to successfully address the adaptation of specific practical courses
with high student–teacher ratios to the new requirements that the COVID-19 pandemic imposes.
The real-world context in this case study is a programming course in a higher education institution in
Spain that had to be urgently adapted to a fully online format due to the pandemic. The contribution
of this case study is two-fold: first, it illustrates how the evaluation of a programming course in a
higher education institution can be successfully transformed to the new requirements by means of a
student-centered automated assessment tool, and second, it analyzes students’ perceptions of the use
of such a tool in the evaluation of the course and their interactions with it.
The rest of this section describes the course context as it was before the pandemic, how it was
transformed due to the new requirements, and the automated student-centered assessment tool that
was used for this transformation. Lastly, the instruments used to collect students’ interactions with the
automated assessment tool and the students’ opinions on its use are detailed.
3.1. Course Context (Pre-COVID-19)
The programming course analyzed in this study is part of the Bachelor’s Degree in Telecommunications
Engineering at UPM (Universidad Politécnica de Madrid). It is a third-year course that accounts for
4.5 ECTS (European Credit Transfer System) credits, which is equivalent to 115–135 h of student work.
In this course, the students learn the basics of web development, including HTML (Hypertext Markup
Language), CSS (Cascading Style Sheets), JavaScript, and more advanced technologies, such as node.js,
express, and SQL (Structured Query Language). The course follows the AMMIL (Active Meaningful
Micro Inductive Learning) Methodology [52]; hence, the complete program is recorded in video
as micro-lessons.
There are nine programming assignments delivered to students through the Moodle platform
used in the course. Students are required to submit all of the assignments, which account for 30% of the
final grade. The remaining 70% corresponds to two written exams: one midterm and one final exam,
each accounting for 35% of the final grade. In order to pass the course, students needed to achieve a
grade greater than or equal to 4 out of 10 in the exams, and obtain a grade of at least 5 out of 10 in the
course final grade.
There were two main reasons for introducing an automated student-centered assessment tool in
this course. In the first place, the course has a high student–teacher ratio because it is a core course,
and all the students pursuing the Telecommunications Engineering degree have to pass it (there are
312 students and seven teachers in total). In the second place, it is very common for students not to
have high programming skills at this stage of their studies, finding it more difficult than other courses.
This latter fact has three implications: in the first place, the number of programming assignments
should be high (multiple and frequent small assignments instead of one final project); in the second
place, students need as much detailed feedback and help as possible, which can be proven to be an
additional motivating factor [53] and can improve the students’ learning experience [37]. Finally,
this feedback should be provided frequently and timely, even immediately (after each execution of the
tool) if possible, allowing the student to continue working on the assignments with the knowledge
of how well he/she is performing and learn from their mistakes, this fact has been widely studied to
improve student performance and promote learning [54–56]. Summing up, a high student–teacher
ratio, combined with a high number of assignments and the suitability of immediate feedback, make the
use of an automated student-centered assessment tool perfect for this kind of course. Due to the fact
that the feedback provided is a central piece of an automated assessment tool, three teachers were in
charge of designing it after reviewing the literature, studying successful case studies and inspecting
similar tools.
Although this programming course has a Moodle platform for distributing the didactic materials
and relies on an automated student-centered assessment tool for the programming assignments, it also
has a high face-to-face load. The teachers dedicate half of a 50 min session to explain in detail each
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assignment, and one or two extra sessions to solve each of them step by step. In addition, face-to-face
tutorials are frequent in this subject, in order to solve doubts and guide students in their work, as they
do not have high programming skills.
3.2. Assessment Transformation
The midterm exam was scheduled for the 16th of March, and the disruption of face-to-face
activities due to the COVID-19 pandemic took place on the 11th of March. Hence, following the
guidelines provided by the head of studies, the midterm exam was canceled and, consequently, the final
exam was worth 70% of the grade. In order to pass the course, the students needed to pass the exam
(by achieving a grade greater than or equal to 5 out of 10) and obtain a grade of at least 5 out of 10 in the
course’s final grade, which was calculated as the weighted sum of the exam and assignments’ scores.
The COVID-19 pandemic not only led to the cancelation of the midterm exam, but also to the need
to transform the face-to-face lessons, the programming assignments and the final exam into a fully
online format. Fortunately, as the complete program was recorded in video as micro-lessons, only some
lessons and tutorials had to be conducted via videoconferencing tools. The programming assignments
were planned to make use of the automated student-centered assessment tool from the beginning
of the course, as explained in the previous section, but as a complementary tool with the support
of the face-to-face sessions (in which the assignments were solved step-by-step) and tutorials. Now,
in order to meet the new requirements, and to play a central role in the course assignments, although
the automated assessment tool was already online-based, the last four programming assignments
that were left when the COVID-19 situation emerged had to be adapted, explaining the problem
statements in a more detailed way, and giving more elaborate feedback to students. This tool also had
to be complemented with the LMS forums and videoconference sessions to substitute for face-to-face
assistance. To sum things up, the automated student-centered assessment tool went from being a
complementary tool in the programming assignments to being the primary tool.
On the other hand, the final exam was a major challenge, since this programming course is a very
practical one, in which the skills and learning outcomes established are impossible to measure with just
an online test. Additionally, students’ perceptions of the assessment characteristics play a positive role
in their learning, resulting in deeper learning and improved learning outcomes. Two characteristics
have special influence: authenticity [57,58] and feedback [59,60]. Hence, the teaching staff of the course
decided to divide the final exam into two parts: the first part—intended to measure the more theoretical
concepts—was a multiple-choice test with 30 questions to be completed in 30 min, whereas the second
part of the exam—intended to measure practical programming skills—was a 40-min programming
assignment that made use of the same automated assessment tool used in the assignments of the course.
The exam had multiple slightly different variants of a similar level of difficulty. Upon launching
the assessment tool for the first time, each user was assigned a version of the exam based on their
credentials. Since this procedure slightly differed from previous exercises, a mock exam was published
days before the actual exam in order to help the students understand how the exam was going to be
performed, and to help them practice generating the problem statement and turning it in. Both parts of
the final exam accounted for 50% of the final exam grade, and each part had to be passed with at least
4 out of 10 points.
To guarantee the validity of the assessment method, all of the changes planned were included in
the subject teaching guides as addenda, following the strategy made public by ANECA [14], and were
notified to the students well in advance of the final exam.
3.3. Description of the Assessment Tool
This section describes the student-centered automated assessment tool used both in the programming
assignments of the course and the second part of the final exam. There is a wide set of assessment
tools for programming assignments available [18,22,24]. However, these tools are rarely used beyond
the institutions in which they were created, because they are difficult to adapt and extend to fit new
Sustainability 2020, 12, 7451 7 of 24
courses [18,61]. The case study presented in this paper is another example of the latter, as the course
staff had recently developed the automated student-centered assessment tool for the programming
assignments, so it was easily extended and adapted to support the new requirements imposed by the
COVID-19 pandemic and the interruption of the face-to-face activities.
The automated student-centered assessment tool used in this case study is called autoCOREctor,
and it consists of a client and a server. The tool was designed to be easily integrated with Learning
Management Systems (LMSs) and Version Control Platforms (VCPs). The LMS was used to store
students’ submissions and grades, which is essential for legal purposes, because learning evidence
must be stored and kept in the course LMS. The VCP was employed to facilitate the management of
the assignments’ problem statements and check the test suites’ integrity to avoid cheating. The main
features of autoCOREctor are the following:
• Student-centered: students start the assessment process. They can view the assignment
specification, and develop and submit a solution for it. For each submission, the tool assesses
the student’s solution, considering the assessment parameters provided by the instructor.
After the assessment process is completed, both the instructor and the students have access
to the assessment results.
• Tests are run locally on the student’s computer: students can work offline, and the score they
obtain locally is later uploaded to the LMS once they decide to submit it, turning it into their
assignment grade.
• Unlimited number of local test runs: students can run autoCOREctor as many times as they wish,
getting immediate feedback about their work, as well as their current grade.
• Unlimited number of submissions: students can submit their score and the solution of the
assignment to the LMS as many times as they want in the timeframe determined by the instructor.
• Penalty for late submissions: instructors can configure a grace period in which students can
submit their assignments with a penalty in their grade.
• The autoCOREctor client is distributed as an NPM (Node Package Manager) package uploaded
to the official NPM repository. This feature facilitates the tool installation and update in case
a new version is released by the teaching staff. The autoCOREctor client is therefore a CLI
(Command Line Interface) tool that can work on any operating system—Linux, Windows or
Mac—indifferently. Also, if any student has any problem with the installation, the client can be
used from free online services that include a terminal, such as Glitch [62].
• Thorough documentation for instructors and students: documentation is made available to
students in the LMS, and the list of available options can be listed through a shell command of the
autoCOREctor client.
• Integrity check of the test suite: autoCOREctor checks that the test suite that is run to obtain the
feedback and grade is the one that the instructor developed.
• Learning analytics: the autoCOREctor client logs all of the interactions that students have with it
as well as the evolution of the resulting grades, uploading all this information to the autoCOREctor
server for further analysis whenever students make a submission.
• Learning analytics dashboards: the autoCOREctor server generates interactive graphs of the
evolution of grades per assignment and student.
• Generation of a scaffold for assignments’ problem statements and a basic test suite with examples
to facilitate instructors in its use in different contexts.
• Possibility to define test cases and to specify the generated feedback, as well as the way in which
the grades of the assignments are calculated in multiple programming languages.
• Secure communications: all of the information that autoCOREctor sends uses secure sockets.
Additionally, all of the assignments, logs, and grades are encrypted before being sent.
The following paragraphs explain the generic design of the autoCOREctor tool and then the
specific deployment we have performed for this case study.
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AutoCOREctor consists of a web server with a connector to send requests to the LMS, a connector
to communicate with the VCP, and a client program that is executed on the students’ computer.
Figure 1 shows the architecture of the system from the point of view of a teacher. The details of the
functionalities of each component and the interaction flow between them are as follows:
1. The teacher creates an empty repository for the assignment in the Version Control Platform.
2. The teacher creates and configures the assignment for the corresponding course in the LMS.
3. The third step is to obtain an assignment template to implement the test suite and the assignment
problem statement. For this, the teacher accesses the autoCOREctor web server interface, where
information about courses and assignments created by the teacher is provided. This information
is retrieved by the LMS Connector which, depending on the LMS and the authentication and
authorization mechanisms available, gathers it directly by the LMS API (Application Programming
Interface) or using a delegated authorization mechanism such as OAuth or OpenID Connect
(Step 3a). Once this is achieved, the teacher can link the repository created in Step 1 to the
specific assignment of the LMS (Step 2). This link is stored in the server database, so no further
interactions are needed with the LMS. As a result of this link, the server creates a template of
the assignment containing the needed metadata of both the repository and the assignment, to be
used later when the students submit their results. Finally, the teacher downloads this template.
4. Using the downloaded template, the teacher creates the assignment’s problem statement and the
test suite that will be run during its development by the students. Depending on the characteristics
of each course, the tests will be made using a specific testing framework. The downloaded
template contains the software libraries needed to develop such tests.
5. When the tests and the assignment problem statement are ready, the teacher uploads them to the
VCP, making it available to students. If the teacher eventually fixes mistakes in the assignment
or wants to create new versions, he/she only has to upload the changes to the VCP and, if the
assignment is open and the students have already started working on it, notify the students to
update the assignment with one Version Control System command.
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2. The teacher creates and configures the assignment for the corresponding course in the LMS.
3. The third step is to obtain an assignment template to implement the test suite and the assignment
problem statement. For this, the teacher accesses the autoCOREctor web server interface, where
information about courses and assignments created by the teacher is provided. This information
is retrieved by the LMS Connector which, depending on the LMS and the authentication and
authorization mechanisms available, gathers it directly by the LMS API (Application
Programming Interface) or using a delegated authorization mechanism such as OAuth or
OpenID Connect (Step 3a). Once this is achieved, the teacher can link the repository created in
Step 1 to the specific assignment of the LMS (Step 2). This link is stored in the server database,
so no further interactions are needed with the LMS. As a result of this link, the server creates a
template of the assignment containing the needed metadata of both the repository and the
assignment, to be used later when the students submit their results. Finally, the teacher
downloads this template.
4. Using the downloaded template, the teacher creates the assignment’s problem statement and
the test suite that will be run during its development by the students. Depending on the
characteristics of each course, the tests will be made using a specific testing framework. The
downloaded template contains the software libraries needed to develop such tests.
5. When the tests and the assignment problem statement are ready, the teacher uploads them to
the VCP, making it available to students. If the teacher eventually fixes mistakes in the
assignment or wants to create new versions, he/she only has to upload the changes to the VCP
and, if the assignment is open and the students have already started working on it, notify the
students to update the assignment with one Version Control System command.
Figure 1. Automated assessment tool architecture from the point of view of the teacher.
On the other hand, Figure 2 shows the architecture from the point of view of the students. The
details of the functionalities of each component and the interaction flow between them are as follows:
1. Students download an assignment from the Version Control Platform after obtaining its specific
repository link from the LMS assignment. It contains the problem statement of the assignment,
the template with the source code to develop it, and the test suite that the teacher has defined.
2. Before starting to develop the assignment’s solution, the students have to download an
Figure 1. Automated assessment tool architecture from the point of view of the teacher.
On the other hand, Figure 2 shows the architecture from the point of view of the students. The details
of the functionalities of each component and the interaction flow between them are as follows:
Sustainability 2020, 12, 7451 9 of 24
1. Students download an assignment from the Version Control Platform after obtaining its specific
repository link from the LMS assignment. It contains the problem statement of the assignment,
the template with the source code to develop it, and the test suite that the teacher has defined.
2.
Before starting to develop the assignment’s solution, the students have to download an
authentication token from the LMS. This token will be used by the LMS Connector in Step 5 to
submit the students’ results to the LMS.
3. While developing the assignment, students can use the autoCOREctor client to run the tests
defined by the teacher as many times as they want, receiving immediate feedback and a score
each time. Every time the assessment tool client is executed, it checks whether the assignment has
been updated in the Version Control Platform (Step 3a). To do this, the client has to be adapted to
use the specific API of the Version Control Platform. Moreover, the client saves a history file with
the evolution of the scores achieved by the student.
4. Students use this same client to submit their results to the autoCOREctor server. During this
process, the server checks that the version of the tests the student is uploading is updated with the
latest version at the VCP (Step 4a). Again, the VCP Connector has to be adapted to the API of the
Version Control Platform. Moreover, in order to ensure the integrity and authenticity of the results,
the autoCOREctor client encrypts and signs the information containing such results with a shared
key with the server. Thus, when receiving the information, the server is in charge of decrypting
the data and validating the signature. Furthermore, the server checks that the tests defined by the
teacher have not been modified by comparing a hash code sent by the client together with the
score with the same hash created from the version available in the VCP. This submission can be
performed as many times as the student wants while the assignment is open, keeping the last
result uploaded.
5. Finally, the server stores the file of the assignment solution, the results, and the history in its
database and sends the score to the LMS using the LMS Connector. If a penalty for late submissions
has been configured by the teacher in the assignment, the score is adapted accordingly in case it
is necessary.
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3. While developing the assignment, students can use the autoCOREctor client to run the tests
defined by the teacher as many times as they want, receiving immediate feedback and a score
each time. Every time the assessment tool client is executed, it checks whether the assignment
has been updated in the Version Control Platform (Step 3a). To do this, the client has to be
adapted to use the specific API of the Version Control Platform. Moreover, the client saves a
history file with the evolution of the scores achieved by the student.
4. Students use this same client to submit their results to the autoCOREctor server. During this
process, the server checks that the version of the tests the student is uploading is updated with
the latest version at the VCP (Step 4a). Again, the VCP Connector has to be adapted to the API
of the Version Control Platform. Moreover, in order to ensure the integrity and authenticity of
the results, the autoCOREctor client encrypts and signs the information containing such results
with a shared key with the server. Thus, when receiving the information, the server is in charge
of decrypting the data and validating the signature. Furthermore, the server checks that the tests
defined by the teacher have not been modified by comparing a hash code sent by the client
together with the score with the same hash created from the version available in the VCP. This
submission can be performed as many times as the student wants while the assignment is open,
keeping the last result uploaded.
5. Finally, the server stores the file of the assignment solution, the results, and the history in its
database and sends the score to the LMS using the LMS Connector. If a penalty for late
submissions has been configured by the teacher in the assignment, the score is adapted
accordingly in case it is necessary.
Figure 2. Automated assessment tool architecture from the point of view of the students.
Some important aspects should be commented on, relating to the reliability and validity of the
assessment process. Regarding reliability, autoCOREctor performs the integrity check of the test
suites that it uses, so all of the students are assessed with the same test suite. It also signs and encrypts
everything that is sent to avoid cheating. With respect to validity, before starting its use in the course,
it was validated among the members of the course staff; some of them completed the assignments
and some corrected the solutions, verifying that the grades were very similar. The same procedure
was followed with random submissions of the final exam
Figure 2. Automated assessment tool architecture from the point of view of the students.
Some important aspects should be commented on, relating to the reliability and validity of the
assessment process. Regarding reliability, autoCOREctor performs the integrity check of the test suites
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that it uses, so all of the students are assessed with the same test suite. It also signs and encrypts
everything that is sent to avoid cheating. With respect to validity, before starting its use in the course,
it was validated among the members of the course staff; some of them completed the assignments
and some corrected the solutions, verifying that the grades were very similar. The same procedure
was followed with random submissions of the final exam, verifying again that the scores reported by
autoCOREctor were similar to those given by the course teachers. This verification was very informal,
and since further research is needed on the validity of the grades generated by automated assessment
systems, it constitutes an interesting work that will be addressed in the near future.
In our case study, we used GitHub [63] as the VCP and Moodle as the LMS. Thus, teachers create
new GitHub repositories for each assignment and recieve a URL that is registered in the autoCOREctor
server linked to the assignment created in Moodle. In order to retrieve the courses and assignments
created by a teacher, we use the Moodle API [64], authenticating teachers by means of their username
and password. As the course in which we evaluated the platform is about Node.js technology, the tool
creates assignment templates with a package.json file that contains metadata about the LMS course
and assignment.
In the case of this course, the tests are written by the teacher using the Mocha [65] and Zombie [66]
frameworks, and the Chai library [67]. The students also need to download an authentication token
from Moodle that is later used by the server to upload their scores. This token is included together
with the students’ email, the score, the test version, the hash for checking the integrity of the tests,
and the signature in a JSON (JavaScript Object Notation) file created by the client.
3.4. Data Collection Instruments
Three instruments were used in this study in order to evaluate the students’ perceptions and their
interactions with autoCOREctor: (1) a survey to collect students’ opinions on the use of autoCOREctor
in the assignments, (2) another survey to collect students’ opinions on the use of this tool in the final
exam, and (3) the tool itself, which automatically records data on students’ interactions with it, in order
to obtain information on students’ usage of the tool.
In order to collect students’ perceptions on using autoCOREctor for the programming assignments
in the course, a survey was conducted after the termination of the last assignment of the course.
This survey was designed by the authors of this article, and was validated by three faculty members.
It included some initial demographic questions, a set of closed-ended questions addressing students’
general opinion and acceptance of the tool, and a list of statements with which they needed to
agree or disagree using a 5-point Likert scale. These questions were aimed at assessing students’
attitudes towards the use of the autoCOREctor, students’ thoughts on its usability and usefulness as an
assessment method, their perceptions on the feedback and grades received, their opinions on the main
features of the tool, and whether they prefer it over other assessment methods. At the end of the survey,
there was a space in which the students could leave suggestions, complaints, and other comments.
Moreover, with the aim of collecting students’ perceptions toward using autoCOREctor for the
final exam of the course, a survey was conducted after the exam. This survey was also designed
by the authors of this article, and was validated by three faculty members. It included some initial
demographic questions, a set of closed-ended questions addressing students’ general opinions and
acceptance of the activity, and a list of statements with which they needed to agree or disagree using a
5-point Likert scale. These questions were similar to the ones posed in the first survey, but they were
aimed at assessing students’ attitudes towards the use of the automated assessment tool in the specific
context of the exam. Once again, at the end of the survey, there was a space in which the students
could leave suggestions, complaints, and other comments.
Lastly, autoCOREctor automatically recorded data on student interactions with it both for the
assignments and the final exam. Specifically, the following data were collected for each of the nine
assignments and for the exam: the number of times each student ran the tests locally, the number of
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times each student submitted their solution to the LMS, and the score they obtained in each execution
of the test suite.
3.5. Data Analysis
The data gathered by the three instruments mentioned earlier were processed using Excel and
Python, along with the Numpy, Scipy and Pandas software packages. A descriptive quantitative
analysis was performed in the resulting datasets.
The survey data were analyzed using mean (M), standard deviation (SD), and median absolute
deviation (MAD), as well as the median (MED) value, which is more representative of the central
tendency in scaled non-normal distributions such as those of Likert-type variables. In addition,
Cronbach’s alpha (α) was calculated in order to assess the internal consistency of both surveys,
confirming their reliability at α > 0.9 for both of them. Furthermore, in view of the reduced number of
responses to the open-ended questions, only an informal qualitative analysis was performed on these.
The usage data logs collected by the automated assessment tool were pre-processed in order to
remove spurious logs, such as the ones generated by the faculty staff when testing the assignments.
Then, the data were aggregated by assignment, and by whether the log corresponded to a local
use of the tool or a submission. The resulting dataset includes the number of average executions
(local and submissions) per student and assignment, as well as the total executions per assignment.
This information allowed us to analyze the evolution of the usage pattern of autoCOREctor throughout
the course, as well as the workload to which autoCOREctor is subjected.
4. Results and Discussion
4.1. Results of the Student Survey on the Use of the Automated Assessment Tool on Programming Assignments
Table 1 shows the results of the student survey conducted after carrying out the programming
assignments of the course, including, for each question, the mean (M), median (MED), standard deviation
(SD), and median absolute deviation (MAD), along with the number of answers (N). The survey was
completed by 85 students (65 men and 20 women), with a median age of 21 (MAD = 1.0). Figure 3
shows the distribution of the students’ responses.
The results of the survey show that the students had a positive overall opinion of the use of the
autoCOREctor automated assessment tool in the programming assignments of the course (MED = 4.0,
MAD = 1.0). In terms of usability, the students strongly believe autoCOREctor was very easy to install
(MED = 5.0, MAD = 0.0) and to use (MED = 5.0, MAD = 0.0). These aspects are of special relevance,
since many students find programming difficult to learn, as evidenced by the high failure rates that
programming courses usually have [68]; thus, using the assessment tool should not pose yet another
difficulty for them.
Students’ opinions diverged when asked whether they agreed with the survey statements that the
feedback provided by autoCOREctor was useful (MED = 3.0, MAD = 1.0), that it was easy to understand
(MED = 4.0, MAD = 1.0), and that it helped them to improve their assignments (MED = 3.0, MAD = 1.0).
One possible reason why not all of the students found the feedback provided by autoCOREctor useful
is probably that it merely pointed out the errors found in their solutions, but did not tell them how
to fix them, which students would have found to be of more utility, despite its being detrimental
to their learning. However, the most likely reason why they did not find the feedback provided by
the tool extremely useful is that the students often make syntax mistakes in their code, which can
prevent autoCOREctor from functioning as expected and cause it to throw default error messages that
can be somewhat cryptic, instead of displaying the ones provided by the teachers for each specific
aspect of the assignment that is not working properly. In this regard, previous studies have also
found that students have difficulties understanding the feedback provided by automated assessment
systems [45], and some of them concluded that the quality of the feedback needs to be improved
in order to increase student acceptance [44,50]. Nonetheless, in this study, most students were very
Sustainability 2020, 12, 7451 12 of 24
confident that they would rather receive the feedback provided by autoCOREctor than no feedback
at all (MED = 5.0, MAD = 0.0), and that if using the tool had been optional, the majority of them
would have chosen to use it without a doubt (MED = 5.0, MAD = 0.0), providing more evidence that,
overall, autoCOREctor was useful for students. In fact, most of them agreed that autoCOREctor helped
them discover errors in their assignment that they did not know they had (MED = 4.0, MAD = 1.0),
which, without autoCOREctor, would only have been possible through manual teacher assessment
and, consequently, infeasible in such a crowded course.
Table 1. Results of the student survey on the use of autoCOREctor on programming assignments.
Question N M MED SD MAD
Q1. What is your general opinion on autoCOREctor on a scale of
1 (Very bad) to 5 (Very good)? 85 3.9 4 0.9 1.0
State Your Level of Agreement with the Following Statements on autoCOREctor on a Scale of
1 (Strongly Disagree) to 5 (Strongly Agree)
Q2. autoCOREctor was easy to install 85 4.5 5.0 0.9 0.0
Q3. autoCOREctor was easy to use 85 4.3 5.0 1.0 0.0
Q4. The feedback provided by autoCOREctor was useful 85 3.3 3.0 1.1 1.0
Q5. The feedback provided by autoCOREctor was easy to understand 85 3.3 4.0 1.1 1.0
Q6. The feedback provided by autoCOREctor helped me improve
my assignments 85 3.5 3.0 1.1 1.0
Q7. I’d rather receive the feedback provided by autoCOREctor than no
feedback whatsoever 85 4.6 5.0 0.9 0.0
Q8. If using autoCOREctor had been optional, I would have chosen to
use it without a doubt 85 4.6 5.0 0.9 0.0
Q9. autoCOREctor has helped me discover errors in my assignments
that I did not know I had 85 3.8 4.0 1.1 1.0
Q10. Having to pass the autoCOREctor tests for each assignment has
made me spend more time in the assignments than I would have if I
had not used it
79 2.9 3.0 1.4 1.0
Q11. autoCOREctor has increased my motivation to work on
the assignments 81 3.5 4.0 1.0 1.0
Q12. Being able to run the autoCOREctor tests repeated times with no
penalty and obtaining instant feedback has made me invest more time
in the course assignments
83 4.3 5.0 1.0 0.0
Q13 The grades provided by autoCOREctor were fair 85 4.3 5.0 1.0 0.0
Q14. Thanks to autoCOREctor I think I got a better grade in the
assignments that I would have without it 85 4.6 5.0 1.0 0.0
Q15. In general, using autoCOREctor has improved my
programming knowledge 84 3.9 4.0 1.1 1.0
Q16. I think by using autoCOREctor I have improved my programming
knowledge more than I would have with a manual assessment 84 4.0 4.0 1.1 1.0
Q17. In general, I believe the use of automated assessment tools such as
autoCOREctor improves the evaluation process of the course
assignments when compared to the classical manual procedure
85 4.6 5.0 0.9 0.0
Q18. I would like to have tools like autoCOREctor in other courses 84 4.7 5.0 0.8 0.0
Indicate How Useful you Find Each of the Following Features of autoCOREctor on a Scale of 1 (Useless) to 5
(Very Useful)
Q19. It allows to run the tests an unlimited number of times 84 4.9 5.0 0.5 0.0
Q20. It has a command to directly upload the assignment to Moodle 84 4.7 5.0 0.8 0.0
Q21. It allows to run the tests locally on the student’s computer 83 4.6 5.0 0.8 0.0
Q22. It provides instant feedback each time the tests are run 84 4.4 5.0 1.0 0.0
Q23. It provides documentation to learn how to use it and the options
it provides 77 3.9 4.0 1.0 1.0
On another note, the students’ opinions diverged regarding whether having to pass the tests
suites provided by autoCOREctor for each assignment made them spend more time working on them
than they would have without the tool (MED = 3.0, MAD = 1.0). However, they strongly agreed with
the fact that being able to run the tests repeatedly and obtaining instant feedback made them invest
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more time (MED = 5.0, MAD = 0.0). On the one hand, since students know their grade at every step of
the way while working on an assignment, they can see how more work translates into a higher grade
in real-time, encouraging them to keep working and dedicating time to the assignment, and to strive
for the best score. In this regard, the students somewhat agreed that autoCOREctor has increased their
motivation to work on the assignments (MED = 4.0, MAD = 1.0). On the other hand, once they reach
the maximum score, they stop working on the assignments and turn them in, whereas, without the
automated assessment tool, they keep testing each assignment manually until they are sure it works
properly, which takes considerably more time. Hence, since the feedback provided by the tool helps
them to identify mistakes, they do not spend as much time testing. Despite the fact that this is generally
regarded as a negative aspect of automated assessment tools, the programming course analyzed in this
study is focused on implementation and does not cover software testing, so the students do not have the
knowledge required to properly develop testing suites for their code anyway. Thus, besides manually
testing their solutions, autoCOREctor is the only resource they have to check if their code is correct.
In addition, the feedback provided by autoCOREctor does not reveal how to fix mistakes, but instead
merely points them out, so they still need to figure out how to fix them by themselves, which also takes
considerable time. It should be noted that, even though the students do not know how to perform
software tests, they are taught how to debug their code using standard development tools so that
they can track down the errors identified by autoCOREctor. In sum, although is not clear if using
autoCOREctor requires students to invest more or less time in the assignments, the results suggest
that they do not spend as much time manually looking for errors as they would without the tool but,
thanks to the instantaneous feedback, they spend more time working on the solution of the assignment
itself and debugging the errors pointed out by the tool.
Moreover, most of the students strongly believed that the grades provided by autoCOREctor were
fair (MED = 5.0, MAD = 0.0). They also very strongly agreed with the statement that—thanks to the
automated assessment tool—they got a better grade in the assignments than they would have without
it (MED = 5.0, MAD = 0.0), which was an expected outcome, since the instant feedback and unlimited
number of attempts allowed them to progressively improve their grades until they were content.
On the one hand, since—by using autoCOREctor—they spent more time working on the assignments
and obtained a better grade than they would have without the tool, it is not surprising that they found
the grades provided by the tool fair. On the other hand, it could be argued that autoCOREctor is
a little too sensitive to typing errors and mishaps since, with just a slight syntax error in the code,
the tool would provide a grade of 0, even if all of the features were correctly implemented. However,
this limitation did not impact the students’ opinions in this case study. Hence, it is likely that the
students’ positive perceptions on the grades provided by autoCOREctor were a result of the unlimited
number of attempts; thus, their opinions apply not to the score calculated by autoCOREctor each time
they ran the tests, but rather to the grade they ultimately obtained for each assignment in the course,
thanks to the unlimited number of attempts and the instant feedback that allowed them to fix the errors
and achieve the best possible score. Consequently, the authors believe that, if the number of attempts
has been limited, students’ perceptions on grading fairness would have been much more negative.
With regard to self-efficacy (i.e., students’ perceived skills [69]), students reported that they
have improved their programming knowledge by using autoCOREctor (MED = 4.0, MAD = 1.0),
even more than they would have with manual assessment (MED = 4.0, MAD = 1.0). These were the
expected outcomes, since autoCOREctor allowed them to recieve feedback on their actions right away,
helping them learn from their mistakes along the way. In this regard, it is worth mentioning that the
piece of feedback that the students receive from autoCOREctor is not only a number, which is usually
the case in manual assessment in large-enrollment courses, but is rather a message that tells them what
is wrong or missing for each part of the assignment, inviting them to fix it. Moreover, the students
strongly believe that the use of automated assessment tools, such as autoCOREctor, improves the
evaluation process of the course assignments when compared to a manual procedure consisting in
manually submitting the code to the LMS and waiting for the teacher to correct it after the deadline
Sustainability 2020, 12, 7451 14 of 24
(MED = 5.0, MAD = 0.0), which is the usual procedure in most courses. Thus, the fact that the feedback
provided by autoCOREctor is instantaneous and actually feed-forward (i.e., feedback that lets students
act upon it, giving them a chance to improve their solutions) is regarded highly by the students, since it
allowed them to learn from their mistakes, as opposed to traditional feedback, which is commonly
not provided in a timely manner, as students usually receive it when they have already forgotten
about how they reached their solution and they are often not allowed to improve their work on the
basis of the feedback received. In essence, it is safe to conclude that the students are satisfied with the
automated assessment tool in this course, and that they would very much like to have similar tools in
other courses (MED = 5.0, MAD = 0.0).
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Q17. In general, I believe the use of automated assessment tools such as
autoCOREctor improves the evaluation process of the course assignments
when compared to the classical manual procedure
85 4.6 5.0 0.9 0.0
Q18. I would like to have tools like autoCOREctor in other courses 84 4.7 5.0 0.8 0.0
Indicate How Useful you Find Each of the Following Features of autoCOREctor on a Scale of 1 (Useless)
to 5 (Very Useful)
Q19. It allows to run the tests an unlimited number of times 84 4.9 5.0 0.5 0.0
Q20. It has a command to directly upload the assignment to Moodle 84 4.7 5.0 0.8 0.0
Q21. It allows to run the tests locally on the student’s computer 83 4.6 5.0 0.8 0.0
Q22. It provides instant feedback each time the tests are run 84 4.4 5.0 1.0 0.0
Q23. It provides documentation to learn how to use it and the options it
provides 77 3.9 4.0 1.0 1.0
Figure 3. Distribution of responses to the student survey on the use of autoCOREctor on
programming assignments.
Figure 3. Distribution of responses to the student survey on the use of autoCOREctor on
programming assignments.
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When asked to rate the different features of autoCOREctor, the students showed a very strong
preference for the ability to run the tests an unlimited number of times (MED = 5.0, MAD = 0.0),
which came as no surprise since, each time the tests were executed, the tool provided them with their
score and a piece of instantaneous custom feedback, as mentioned earlier. The second most highly
rated aspect was the command to directly upload the assignment to the LMS (MED = 5.0, MAD = 0.0).
Since this command allowed the students to submit their assignments automatically, without directly
interacting with the LMS, it was very convenient for them, and prevented them from making mistakes
when packaging and uploading the assignment. The students highly valued being able to run the
tests locally on their computer (MED = 5.0, MAD = 0.0), since not having to upload the assignment
to a remote server each time they want to receive feedback saves a lot of time and avoids errors
derived from running the code in different execution environments. Predictably, the students highly
appreciated the instant feedback received each time the tests were run, as discussed earlier (MED = 5.0,
MAD = 0.0). Lastly, the students also found that the documentation provided to help them install and
use autoCOREctor was useful (MED = 4.0, MAD = 1.0). Since the tool is so easy to install and use,
the documentation was not needed in most cases, so it comes as no surprise that some students might
not have used it, or found it superfluous. As can be seen in Figure 3, overall, the students’ opinions on
all of the features of autoCOREctor are very positive, and there are barely any negative responses for
any of them.
In the last field of the survey—reserved for complaints, suggestions and other comments—the
students confirmed that autoCOREctor motivated them to keep trying to get a better score and made
working on the assignment more fun. Moreover, they also stated that autoCOREctor was useful when
finding the mistakes they made along the way. Some students complained about the quality of the
feedback received, saying it was too generic. It should be noted that this feedback depends on the
specific test suite that is being executed, and is not a limitation of the automated assessment tool itself.
Moreover, giving feedback that is too specific, telling students how to fix their mistakes, would prevent
them from reaching the solution by themselves. As mentioned, although the feedback provided by
autoCOREctor informs students of what is wrong or missing in their assignments, they still need to
find which part of their code is erroneous or incomplete, and figure out how to fix it. This is indeed a
crucial part of learning programming since it promotes self-assessment, making students develop the
highest level of Bloom’s taxonomy, which deals with evaluation [70].
4.2. Results of the Student Survey on the Use of the Automated Assessment Tool in the Final Exam
The results of the student survey conducted after using autoCOREctor in the final exam of the course
are shown in Table 2, including, for each question, the mean (M), median (MED), standard deviation
(SD), and median absolute deviation (MAD), along with the number of answers (N). The survey was
completed by 45 students (35 men and 10 women), with a median age of 21 (MAD = 1.0). Figure 4
shows the distribution of the students’ responses.
According to the students, autoCOREctor was easy to use in the exam (MED = 4.0, MAD = 1.0).
In fact, one of the main reasons that encouraged the faculty staff to use autoCOREctor in the exam
was precisely that students were already accustomed to this tool, and thus incorporating it into the
exam would not bring any additional difficulty to those students who spent time using autoCOREctor
throughout the course. The main difference between the use of autoCOREctor in the assignments and
in the exam was that, in the exam, the tool provided a different problem statement for each student
from among several options, making it more difficult for them to copy from one another. In this
regard, the students stated that generating their problem statement was very easy as well (MED = 5.0,
MAD = 0.0).
Regarding feedback, the students’ opinions were similar to the ones in the previous survey.
It should be mentioned that autoCOREctor was not used in the final exam merely as an instrument to
measure students’ competence, but rather it allowed the transformation of the exam (traditionally a
summative assessment activity) into a learning-oriented assessment activity, since the students were
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provided with the same type of feedback as in the programming assignments as well as an unlimited
number of attempts to solve the exam. The only additional constraints of using autoCOREctor in the
exam compared to its use in the assignments, apart from the different problem statements mentioned
earlier, were that the students were given 40 min to complete their task (instead of several weeks),
and that they could not speak to each other during the exam. When asked about the feedback provided
by autoCOREctor during the exam, most of the students perceived it as useful (MED = 4.0, MAD = 1.0)
and easy to understand (MED = 4.0, MAD = 1.0), and stated that it helped them to improve their solution
(MED = 4.0, MAD = 1.0), although there were many students that thought otherwise. One possible
explanation is that, since the exam was similar to the programming assignments, the students that had
reviewed for the exam were confident of their solution, and used autoCOREctor only to verify that
they were not forgetting to implement any feature and that they did not have any typos that could
cause the autoCOREctor tests to fail. In fact, once again, using autoCOREctor allowed some of them to
discover errors in their exam that they did not know they had (MED = 4.0, MAD = 1.0). By finding
out the errors and missing features in their solutions before submitting, the students were able to use
the knowledge they already had to amend those issues. This chance to incrementally improve their
solutions is usually not given to students in final exams, at least definitely not in paper-based ones.
Thus, using autoCOREctor allowed the students not to lose points because of a missing feature or
small mistake, and thus they obtained a grade that was more representative of their actual knowledge,
and that reflects their acquired skills better than a classic paper-based exam would. In fact, as far as
grades are concerned, the students thought the grades provided by autoCOREctor were fair (MED = 5.0,
MAD = 0.0), and they believed that they obtained a somewhat better grade in the practical final exam
than if they had not used the tool (MED = 4.0, MAD = 1.0), since they would not have had any sort of
feedback during the exam, and would have had to rely solely on their manual tests. That is probably
one of the reasons why they stated that, if using autoCOREctor had been optional in this test, most of
them would have chosen to use it anyway (MED = 5.0, MAD = 0.0).
Table 2. Results of the student survey on the use of the automated assessment tool in the final exam.
Question N M MED SD MAD
State Your Level of Agreement with the Following Statements on autoCOREctor on a Scale of
1 (Strongly Disagree) to 5 (Strongly Agree)
Q1. autoCOREctor was easy to use 45 4.0 4.0 0.9 1.0
Q2. Generating my problem statement was easy 45 4.6 5.0 0.7 0.0
Q3. The feedback provided by autoCOREctor was useful 43 3.8 4.0 1.1 1.0
Q4. The feedback provided by autoCOREctor was easy to understand 43 3.6 4.0 1.1 1.0
Q5. The feedback provided by autoCOREctor helped me improve my
solution to the exam 42 3.5 4.0 1.4 1.0
Q6. autoCOREctor has helped me discover errors in my exam that I did
not know I had 43 3.5 4.0 1.4 1.0
Q7. The grades provided by autoCOREctor were fair 45 4.2 5.0 1.3 0.0
Q8. Thanks to autoCOREctor I got a better grade in the practical final
exam than if I had not used autoCOREctor 45 3.8 4.0 1.4 1.0
Q9. If using autoCOREctor had been optional in this exam, I would
have chosen to use it 43 4.4 5.0 1.0 0.0
Q10. I think autoCOREctor is an adequate tool for a practical
programming exam 45 4.1 4.0 1.0 1.0
Q11. The final exam using autoCOREctor adequately assesses the
competences attained during the course 45 3.8 4.0 1.1 1.0
Q12. autoCOREctor assesses practical competences better than
quiz-based tests or open-ended questions 44 4.2 4.0 0.9 1.0
Q13. I prefer to take a practical test using autoCOREctor than an oral
exam via videoconference 45 4.6 5.0 0.8 0.0
Q14. If there were a face-to-face practical final exam in the computer
laboratory, I would like it to be based in autoCOREctor 42 4.5 5.0 0.8 0.0
Q15. I would like the practical exams of other courses to involve a tool
like autoCOREctor 45 4.2 4.0 1.0 1.0
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Q15. I would like the practical exams of other courses to involve a tool like
autoCOREctor 45 4.2 4.0 1.0 1.0
Figure 4. Distribution of responses to the student survey on the use of autoCOREctor in the final
exam.
According to the students, autoCOREctor was easy to use in the exam (MED = 4.0, MAD = 1.0).
In fact, one of the main reasons that encouraged the faculty staff to use autoCOREctor in the exam
was precisely that students were already accustomed to this tool, and thus incorporating it into the
exam would not bring any additional difficulty to those students who spent time using
autoCOREctor throughout the course. The main difference between the use of autoCOREctor in the
assignments and in the exam was that, in the exam, the tool provided a different problem statement
for each student from among several options, making it more difficult for them to copy from one
another. In this regard, the students stated that generating their problem statement was very easy as
well (MED = 5.0, MAD = 0.0).
Regarding feedback, the students’ opinions were similar to the ones in the previous survey. It
should be mentioned that autoCOREctor was not used in the final exam merely as an instrument to
measure students’ competence, but rather it allowed the transformation of the exam (traditionally a
summative assessment activity) into a learning-oriented assessment activity, since the students were
provided with the same type of feedback as in the programming assignments as well as an unlimited
number of attempts to solve the exam. The only additional constraints of using autoCOREctor in the
exam compared to its use in the assignments, apart from the different problem statements mentioned
earlier, were that the students were given 40 min to complete their task (instead of several weeks),
and that they could not speak to each other during the exam. When asked about the feedback
provided by autoCOREctor during the exam, most of the students perceived it as useful (MED = 4.0,
MAD = 1.0) and easy to understand (MED = 4.0, MAD = 1.0), and stated that it helped them to improve
their solution (MED = 4.0, MAD = 1.0), although there were many students that thought otherwise.
One possible explanation is that, since the exam was similar to the programming assignments, the
students that had reviewed for the exam were confident of their solution, and used autoCOREctor
only to verify that they were not forgetting to implement any feature and that they did not have any
typos that could cause the autoCOREctor tests to fail. In fact, once again, using autoCOREctor
Figure 4. Distribution of responses to the student survey on the use of autoCOREctor in the final exam.
Overall, the results of the survey show that the students think autoCOREctor is an adequate tool
for a practical programming test (MED = 4.0, MAD = 1.0). The students somewhat agree with the
statement that the final exam powered by this tool adequately assessed the competences attained
during the course (MED = 4.0, MAD = 1.0), at least more so than quiz-based tests or open-ended
questions (MED = 4.0, MAD = 1.0), which in itself is a great outcome, since it was the main aim of
using autoCOREctor. Most of the students also strongly agreed that they prefer taking a practical test
using autoCOREctor, rather than an oral exam via videoconference (MED = 5.0, MAD = 0.0), probably
because they do not undergo so much pressure as in the latter. Furthermore, the majority of students
strongly agreed that, in the event of a face-to-face practical final exam in the computer laboratory,
they would like it to be based on autoCOREctor as well (MED = 5.0, MAD = 0.0). These results
highlight that the assessment method used in the course is not only adequate for distance scenarios,
but for face-to-face ones as well. This is of special relevance, since there is very little certainty as to
what the future has in store regarding going back to school or keeping the remote model, so adopting
flexible solutions is the key to being able to switch from one scenario to another overnight. Moreover,
this approach is easily transferable to other disciplines. In fact, most of the students agreed that the
practical exams of other courses should involve a tool like autoCOREctor (MED = 4.0, MAD = 1.0).
In the space reserved for comments, suggestions, and complaints, the students reiterated that they
were satisfied with autoCOREctor. However, some of them brought up the issues regarding feedback
that were discussed in the previous subsection. In fact, one student said that the limited time available
in the exam prevented him from finding the errors pointed out by autoCOREctor. Thus, if feedback
was important for students in programming assignments, in the context of a final exam, in which time
is limited, the importance of feedback is even greater, since it can make a huge difference in students’
grades, and in their overall experience of using the tool during the exam. The amount of feedback
provided should be carefully selected in order to let students know that something is wrong or missing,
whilst also allowing them to realize their own mistakes and fix them.
Sustainability 2020, 12, 7451 18 of 24
4.3. Usage Data Collected by the Automated Assessment Tool
In order to study students’ usage pattern of autoCOREctor, the data collected by the tool itself are
provided in Table 3. These data include, for each of the nine programming assignments, for the exam,
and overall: the average number of local executions of autoCOREctor per student, the total number of
local executions of autoCOREctor overall, the average number of submissions per student, and the
total number of submissions overall.
Table 3. Students’ usage data of the automated assessment tool.
Assignment Average Local
Executions per Student
Total Local
Tests
Average Submissions
per Student
Total
Submissions
Assignment 1 22.6 6605 1.1 331
Assignment 2 10.2 3056 1.1 316
Assignment 3 16.5 4623 1.2 329
Assignment 4 10.9 3059 1.1 303
Assignment 5 21.1 5982 1.3 361
Assignment 6 11.0 2997 1.1 295
Assignment 7 9.1 2711 1.2 334
Assignment 8 7.3 2954 1.1 306
Assignment 9 8.2 2092 1.1 285
Overall Assignments 13.1 3787 1.1 318
Final Exam 14.2 2947 2.2 441
As can be extracted from the data, the number of local executions of the test suites varied greatly
among the different assignments, since each one of them had a different level of difficulty and number
of features that students needed to develop. The first assignment was the one with more test executions,
which is an expected result, since the students were still getting acquainted with autoCOREctor.
When the COVID-19 pandemic struck, the students were working on Assignment 5. The data show
that the number of executions on this particular assignment increased compared to the preceding
and subsequent ones, but since then the trend was downwards. In view of the great differences
among the number of executions of the different assignments, this decline cannot be attributed to
the pandemic, since it might be due to other causes, such as the growing programming expertise
of the students, for instance. Overall, the number of local executions of the test suites adds up to
3787 per assignment on average (13.1 per student on average), and 2947 (14.2 per student on average)
in the final exam. These figures represent the number of times that the students’ assignments were
evaluated by autoCOREctor, which is by no means close to what it would be with manual assessment.
In addition, the fact that the test suites run in the students’ computers makes the tool more scalable,
since all of the computational load needed to run the tests is removed from the server, which only
needs to handle submissions.
On another note, the average number of submissions per student and assignment is close to one.
This is the number of times that the students turned in their assignments once they were satisfied with
their grades. As can be seen, in the programming assignments, the students mostly waited until they
got their desired grade before submitting, whereas in the case of the final exam, this number doubled.
The reason for this is that, during the exam, the students made a submission as soon as they got a
passing grade in order to secure it and, after that, they kept working towards achieving a better score,
and submitted their work again before the time was up. Overall, the data reveal that the students
made extensive use of autoCOREctor, and that the design of the tool made it possible to withstand the
high demand of the students.
5. Conclusions and Future Work
This article presents a case study describing the transformation of the assessment method of a
programming course in higher education into a fully online format during the COVID-19 pandemic,
Sustainability 2020, 12, 7451 19 of 24
by means of a student-centered automated assessment tool called autoCOREctor. This tool was
recently developed by the teaching staff of the programming course used in this case study, so it was
easily extended and adapted to be used for both the programming assignments and the final exam
under the new requirements that the COVID-19 pandemic imposed. The use of a student-centered
automated assessment tool, not only for the assignments but also for the final exam, constitutes a novel
contribution of this article, and could help the teachers of related courses to take the same approach
under the same or similar circumstances. To evaluate the new assessment method, we studied students’
interactions with the tool, as well as students’ perceptions, as measured with two different surveys:
one for the programming assignments and one for the final exam. The results show that students’
perceptions of the assessment tool were positive. Previous research works [57–60] state that students’
perceptions play a positive role in their learning, resulting in deeper learning and improved learning
outcomes. Two assessment characteristics have a special influence on students’ effective learning:
authenticity [57,58] and feedback [59,60]. Regarding the former, the students stated that autoCOREctor
assessed practical competences better than quiz-based tests or open-ended questions, and, in general,
that using autoCOREctor improved their programming knowledge. When asked about the latter,
they stated that the feedback provided was useful and easy to understand, and that they would rather
receive the feedback provided by autoCOREctor than no feedback whatsoever. One important concern
should be stated about authenticity: using an automated assessment tool is not as authentic as a
practical exam where students do not have any feedback or help from an assessment tool and they
have to debug their programs to find their errors. This is something that should be considered by
the teaching staff before using this kind of tool. In this case study, it was an easy decision, as in this
context the students do not have high programming skills, and software testing is out of the scope of
the course.
Based on the evidence that is presented in this study, it can be suggested that student-centered
automated assessment systems can be a great help for students when appropriately integrated into
the teaching method of the course. The students stated that, if using the tool had been optional,
they would have chosen to use it without a doubt, and they would like other courses to involve a tool
like autoCOREctor. Furthermore, they assert that they dedicated more time to the assignments and
that they obtained better grades thanks to the tool. Finally, the generated grades were considered
fair by them, both in the exam and in the programming assignments. On the one hand, this is an
important result, as fairness has proven to be positively correlated with student motivation and
effective learning [71], but, on the other hand, it should be further researched, as in our case study we
have not delved deeper into it.
Since autoCOREctor is a versatile tool that can be adapted to different scenarios, in this case study,
we took advantage of this versatility to be able to urgently change the course assessment method and
adapt it to a fully online format in a timely manner. Programming assignments and exams are part of
the course assessment, but they have different characteristics; the former is a formative assessment and
the latter is a summative assessment, the main difference between them being whether the student
receives feedback and how elaborate this feedback is [11]. By using autoCOREctor, the teacher is the
one in charge of writing the feedback received by students, which can be very detailed, revealing to
the student how to solve the problem found or where to look for a possible solution (which is more
adequate in the programming assignments), or it can be more scarce, only showing the error found
and letting students apply their knowledge to solve the problem (which is more suited to exams).
Regarding the use of an automated student-centered assessment tool in the exam, the experience
reported in this article constitutes another original contribution to the existing body of knowledge,
as no work has been found in the literature reporting this specific use of an automated assessment
system. For this particular use, several advantages and disadvantages were identified in this case study.
In the first place, the main advantage is that it enables the assessment of practical competences for
crowded courses in remote scenarios that could not be measured with an online test alone, applying the
same assessment criteria to all of the students taking the exam. The tool allows the teachers of the
Sustainability 2020, 12, 7451 20 of 24
course to control and monitor the whole assessment process, providing them with learning analytics
that can be used to improve future editions, which is a great advantage. This advantage can also pose
a disadvantage, since the whole exam relies on the tool and, if it fails, the exam cannot be completed.
To mitigate this drawback, a mock exam should be carried out days before the real exam, and an
alternative submission method should be enabled in the Moodle platform in case the tool server hangs
up or freezes. Additionally, a videoconference room can be facilitated for students during the exam,
in order to solve any technical problem they might find. Another great advantage is that it has proven
to be flexible enough to be used in both scenarios—face-to-face and fully online—making it a very
adequate tool to be used over the next few years, in which the COVID-19 pandemic is still threatening
to interrupt face-to-face activities again. In Spain, the Ministry of Universities has distributed some
recommendations and guidelines to adapt next year’s course to what they call the ‘new normal’ in the
presence of COVID-19 [72]. These recommendations include an enhanced digitization strategy and
teaching method adaptability, being able to switch from face-to-face to remote overnight. One final
characteristic identified which could bring an advantage is that, since the autoCOREctor client is
executed on students’ computers, the grade is generated in said environment, avoiding software
differences between the student’s environment and the assessment environment, which could otherwise
make the grade different between both scenarios (as might happen in instructor-centered assessment
tools). However, at the same time, this can pose a security issue if students hack the tool and obtain a
grade without solving the problem statements that were set out. The teaching staff has not identified
any security pitfalls to date, but this could be an interesting future work to analyze it through.
With all these concerns in mind, the teaching staff of the course is convinced of using it over the
coming years, regardless of whether classes are face-to-face or remote. Moreover, if the pandemic does
not subside, and its requirements are sustained over time, the use of automated assessment systems
for both assignments and exams can play an important role in practical courses.
Since autoCOREctor has proven to be an effective tool for use in a crowded programming course
at a higher education institution, another interesting future research topic would be to analyze its
involvement in the assessment of the most crowded courses nowadays: MOOCs (Massive Open Online
Courses). Special attention should be paid to students’ perceptions of the tool and their use of it.
Besides this, in MOOCs about programming, the use of student-centered automated assessment tools
as an alternative to the traditional method of assessment in general-purpose MOOCs (i.e., online tests
and peer-to-peer evaluation) could be further researched, determining whether the use of these tools
can bring any advantage and provide an effective assessment method.
6. Limitations
Several limitations of this case study should be noted. First of all, the surveys were only validated
for internal consistency using Cronbach’s Alpha, and were reviewed by the members of the course staff
(several of them e-learning experts), but not further validations—such as principal components analysis
or a pilot test—were performed. Second of all, as happens with all home assignments, although the
student introduces his/her private credentials (i.e., email and token) in the tool, it cannot be ensured
that the student is the one doing the assignment. The methods to assure this might also constitute an
interesting future work. Finally, additional and more robust conclusions could be drawn if the tool
were used in another scenario or context, such as another practical course that allowed us to obtain
comparable data.
Author Contributions: Conceptualization: E.B., S.L.-P., Á.A., A.G.; software: E.B., S.L.-P., Á.A.; validation: E.B.,
S.L.-P., Á.A., J.F.S.-R., J.Q.; data curation: S.L.-P.; writing—original draft preparation: E.B., S.L.-P., Á.A., A.G.;
writing—review and editing: E.B., S.L.-P., Á.A., J.F.S.-R., A.G.; supervision: J.Q. All authors have read and agreed
to the published version of the manuscript.
Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflict of interest.
Sustainability 2020, 12, 7451 21 of 24
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