Scalable Autograder and LMS Integration

Danutama, Karol; Liem, Inggriani

doi:10.1016/j.protcy.2013.12.207

Cited by 23 publications

(11 citation statements)

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“…Testing whether a program functions according to the given requirements is the most common approach to grade programs. Traditionally, these tests consist of running the program on a set of test cases, having the input to feed the program and the expected output to which the obtained is compared to [6,61,77,241]. This is called the output comparison (OC) method.…”

Section: Testing Techniques (Rq2)mentioning

confidence: 99%

“…Tool / Reference Type Language Availability Interoperability URL Web-CAT [75] WP Multi OS LTI + Eclipse + BlueJ https://web-cat.github.io/ PASS [255] WP C/C++, Java A https://www.cs.cityu.edu.hk/~passweb Kattis [77] WP Multi A https://open.kattis.com ECSpooler [13] WS Multi OS https://github.com/collective/ECSpooler STAGE (CodeCover) [172] PG Java, COBOL OS Moodle plugin http://codecover.org JACK [30,136,227] WP Multi A LTI + Eclipse https://jack-demo.s3.uni-due.de URI Online Judge [32] WP Multi A https://www.urionlinejudge.com.br CodeAssessor [241] WP C/C++ VPL [203] PG Multi OS Moodle plugin https://vpl.dis.ulpgc.es/ CloudCoder [108] WP C/C++, Java, Python, Ruby OS https://github.com/daveho/CloudCoder CodeRunner [60] PG Multi OS Moodle plugin https://github.com/trampgeek/moodle-qtype_coderunner JPLAS [83][84][85] WP 1 Java Eclipse LX [61] WS Multi Modiied Moodle Testovid within Protus [242] WP Java OS https://github.com/ivanpribela/testovid TestMyCode [194] WP Multi OS NetBeans https://github.com/testmycode/tmc-server CodeWorkout [74] WP C++, Java, Python, Ruby OS LTI https://github.com/web-cat/code-workout Arena [184] WP Imp/OO Git JavAssess (ASys) [115] LB Java OS http://personales.upv.es/josilga/ASys CodeOcean [226] WP Multi OS LTI https://github.com/openHPI/codeocean Jutge.org [176] WP Multi OS https://github.com/jutge-org GradeIT (PrUTOR) [62,173] CD C A https://prutor.ai neoESPA [148,150] WP C/C++, Java, Python A http://neoespa.pusan.ac.kr Enki (Mooshak) [170] WP Multi OS LTI + Eclipse https://mooshak2.dcc.fc.up.pt ProgEdu [50,252] CD Java OS Git https://github.com/ProgEdu-Organization/ProgEdu Aristotle [2] ...…”

Section: Table 3 General Information About the Selected Toolsmentioning

confidence: 99%

See 1 more Smart Citation

Automated Assessment in Computer Science Education: A State-of-the-Art Review

Paiva

Leal

Figueira

2022

ACM Trans. Comput. Educ.

110

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Practical programming competencies are critical to the success in computer science education and go-to-market of fresh graduates. Acquiring the required level of skills is a long journey of discovery, trial and error, and optimization seeking through a broad range of programming activities that learners must perform themselves. It is not reasonable to consider that teachers could evaluate all attempts that the average learner should develop multiplied by the number of students enrolled in a course, much less in a timely, deeply, and fairly fashion. Unsurprisingly, exploring the formal structure of programs to automate the assessment of certain features has long been a hot topic among CS education practitioners. Assessing a program is considerably more complex than asserting its functional correctness, as the proliferation of tools and techniques in the literature over the past decades indicates. Program efficiency, behavior, readability, among many other features, assessed either statically or dynamically, are now also relevant for automatic evaluation. The outcome of an evaluation evolved from the primordial boolean values to information about errors and tips on how to advance, possibly taking into account similar solutions. This work surveys the state-of-the-art in the automated assessment of CS assignments, focusing on the supported types of exercises, security measures adopted, testing techniques used, type of feedback produced, and the information they offer the teacher to understand and optimize learning. A new era of automated assessment, capitalizing on static analysis techniques and containerization, has been identified. Furthermore, this review presents several other findings from the conducted review, discusses the current challenges of the field, and proposes some future research directions.

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Section: Testing Techniques (Rq2)mentioning

confidence: 99%

Section: Table 3 General Information About the Selected Toolsmentioning

confidence: 99%

Automated Assessment in Computer Science Education: A State-of-the-Art Review

Paiva

Leal

Figueira

2022

ACM Trans. Comput. Educ.

110

View full text Add to dashboard Cite

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“…Pembelajaran tidak hanya bisa dilakukan pada komputer desktop saja, namun dapat juga dilakukan menggunakan perangkat bergerak (mobile) (Han and Shin, 2016). Pembelajaran pemrograman juga dapat dilakukan menggunakan sistem E-Learning (Danutama and Liem, 2013;Yulianto and Liem, 2014).…”

Section: Pendahuluanunclassified

Penggalian Perilaku Pemain dalam Penentuan Tipe Permainan pada E-Learning Pemrograman Berbasis Gamification

Priyambadha

Pradana

Bachtiar

2020

JTIIK

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Salah satu kompetensi utama yang harus dimiliki oleh lulusan dari jurusan atau program studi bidang keilmuan komputer adalah kemampuan programming (membuat program). Berbagi informasi untuk meningkatkan kualitas pembelajaran pemrograman telah dilakukan di banyak kampus di Indonesia. Salah satunya adalah penerapan media pembelajaran online atau disebut juga sebagai E-Learning. Salah satu sistem pembelajaran yang paling umum, yang didukung oleh teknologi informasi, adalah e-learning. Namun, banyak juga sistem e-learning tidak mencapai tujuan yang diinginkan karena ketidakpatuhan dan kurangnya pengetahuan tentang teknik dan metode untuk pengembangan sistem informasi online. Tujuan yang dicapai antara lain efisiensi, efektifitas, motivasi dan keterlibatan siswa. Pendekatan gamification dapat digunakan untuk meningkatkan beberapa hal tersebut demi tercapainya tujuan pembelajaran online. Dalam sistem Code Maniac, terdapat beberapa elemen gamification yang digunakan, yaitu poin pengalaman, poin aktivitas dan badge. Namun, hal tersebut terbukti masih kurang meningkatkan motivasi mahasiswa dalam menggunakan Code Maniac. Pendekatan player-centric memungkinkan sistem menyesuaikan gameplay yang sesuai dengan gaya bermain seorang pemain. Untuk dapat mewujudkan sebuah media pembelajaran yang berorientasi pada pemain, maka dibutuhkan sebuah mekanisme untuk mengenali karakteristik pemainnya. Pada penelitian ini menekanan metode yang digunakan untuk menggali perilaku permainan. Penggalian pola dilakukan pada data log proses per pemain dan per sesi yang berjumlah 136 proses. Kemudian proses tersebut dikelompokan sesuai dengan kedekatan atau kesamaan karakteristik bermain. Hasil penentuan jumlah kelompok yang paling optimal adalah k=2 dan k=3. Untuk itu pengelompokan dilakukan dan menghasilkan 2 kelompok dan 3 kelompok data. Kelompok tersebut dapat menjadi dasar untuk untuk penentuan gameplay pada Code Maniac. AbstractOne of the main competencies that must be possessed by graduates of departments or study programs in computer science is programming skills (making programs). Sharing information to improve the quality of programming learning has been done on many campuses in Indonesia. One of them is the application of online learning media or also called the E-Learning.. One of the most common learning systems, supported by information technology, is e-learning. However, many e-learning systems do not achieve the desired goals because of non-compliance and lack of knowledge about techniques and methods for developing information systems online. The objectives achieved include efficiency, effectiveness, motivation and student involvement. The gamification approach can be used to improve some of these things in order to achieve online learning goals. In the Code Maniac system, there are several gamification elements used, namely experience points, activity points and badges. However, this proved to be still lacking in increasing student motivation in using Code Maniac. The player-centric approach allows the system to adjust the gameplay to suit a player's playing style. To be able to realize a player-oriented learning media, a mechanism is needed to recognize the characteristics of the players. In this study emphasizes the methods used to explore game behavior. Pattern mining is performed on process log data per player and per session which amounts to 136 processes. Then the process is grouped according to the closeness or similarity of playing characteristics. The results of determining the most optimal number of groups are k = 2 and k = 3. For this reason, grouping is done and produces 2 groups and 3 groups of data. The group can be the basis for determining the gameplay in Code Maniac.

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“…Grouping students according to their level of competence will make teachers easier to monitor the each student's ability. This grouping assignment will be more directed in accordance with the target [19], [20]. Data grouping is the process of extracting patterns that were previously unknown, valid, useful, and hidden from large data sets.…”

Section: Introductionmentioning

confidence: 99%

Integrating k-means clustering into automatic programming assessment tool for student performance analysis

Harimurti

Ekohariadi²,

Munoto³

et al. 2021

IJEECS

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Computer programming is a subject involving a large number of logic programming activities. A programmer is compulsory to master skills of algorithms, logic, and programming language to conduct programming. An automatic programming assessment tool is an automated tool used to assist instructors in assessing programming tasks. The technology used in this application is open-source based with an evaluation module that will evaluate the sent program code, assessment, and classification. The evaluation results were then processed in the assessment module, where a comparison process with the test case was performed along with the point calculation. The classification module was used to divide students into five groups based on the point of each practicum. This study used k-means clustering classification method. The entities included were lecturers, assistants, students, and compilers. This application had 2 levels of users namely admin and students. Scoring results were then used in the process of determining the classification of student’s performance based on the k-means clustering method. In connection with the classification test results with three iterations, three practicum scores resulted that the classification process was successfully carried out with student’s performance divided into five groups covering very good, good, sufficient, less, and very less. The data used in the clustering process consisted of 41 students with 10 attributes which were then grouped into 3 groups (clusters).

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Scalable Autograder and LMS Integration

Cited by 23 publications

References 1 publication

Automated Assessment in Computer Science Education: A State-of-the-Art Review

Automated Assessment in Computer Science Education: A State-of-the-Art Review

Penggalian Perilaku Pemain dalam Penentuan Tipe Permainan pada E-Learning Pemrograman Berbasis Gamification

Integrating k-means clustering into automatic programming assessment tool for student performance analysis

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