Combining program visualization with programming workspace to assist students for completing programming laboratory task

Numerous Program Visualization tools (PVs) have been developed for assisting novice students to understand their source code further. However, none of them are practical to be used in the context of completing programming laboratory task; students are required to keep switching between PV and programming workspace since PV’s features are considerably limited for developing programming solution from scratch. This paper combines PV with programming workspace to handle such issue. Resulted tool (which is named PITON) has 13 features extracted from PythonTutor (a program visualization tool), PyCharm (a programming workspace), and student’s feedbacks about PythonTutor. According to think-aloud and user study, PITON is more practical to be used than a combination of PythonTutor and PyCharm. Further, its features are considerably helpful; students rated these features as useful and frequently usedPeer Reviewe

Bloom-epistemic and sentiment analysis hierarchical classification in course discussion forums

Online discussion forums are widely used for active textual interaction between lecturers and students, and to see how the students have progressed in a learning process. The objective of this study is to compare appropriate machine-learning models to assess sentiments and Bloom’s epistemic taxonomy based on textual comments in educational discussion forums. The proposed method is called the hierarchical approach of Bloom-Epistemic and Sentiment Analysis (BE-Sent). The research methodology consists of three main steps. The first step is the data collection from the internal discussion forum and YouTube comments of a Web Programming channel. The next step is text preprocessing to annotate the text and clear unimportant words. Furthermore, with the text dataset that has been successfully cleaned, sentiment analysis and epistemic categorization will be done in each sentence of the text. Sentiment analysis is divided into three categories: positive, negative, and neutral. Bloom’s epistemic is divided into six categories: remembering, understanding, applying, analyzing, evaluating, and creating. This research has succeeded in producing a course learning subsystem that assesses opinions based on text reviews of discussion forums according to the category of sentiment and epistemic analysis

Utilising pair programming to enhance the performance of slow-paced students on introductory programming

Due to its high failure rate, Introductory Programming has become a main concern. One of the main issues is the incapability of slow-paced students to cope up with given programming materials. This paper proposes a learning technique which utilises pair programming to help slow-paced students on Introductory Programming; each slow-paced student is paired with a fast-paced student and the latter is encouraged to teach the former as a part of grading system. An evaluation regarding that technique has been conducted on three undergraduate classes from an Indonesian university for the second semester of 2018. According to the evaluation, the use of pair programming may help both slow-paced and fast-paced students. Nevertheless, it may not significantly affect individual academic performancePeer Reviewe

The impact of developing a blended learning sub-system on students online learning engagement

In this research, we show a development process of engagement sub-systems in a blended-learning management system and evaluate the impact of student interaction in the whole system. We develop special sub-systems for engagement purposes via forum, course rating, and class assignment modules. During the system development process, we employ continuous improvement methodology which helps to shorten the software delivery time without disturbing the overall operation. We evaluate the impact of engagement processes in terms of behavioral, emotional and cognitive aspects. Our evaluation results show that by employing the engagement sub-systems we have increased a 0.30 satisfaction point on average (1-5 Likert scale) for 11 evaluation survey questions distributed to 305 students during 2 times evaluation period. Another interesting finding from the surveys is that behavioral (discussion forum and attendance list sub-system) and cognitive (course rating sub-system) aspects have great influences for the students’ activities (class assignment sub-system) which finally has a great impact on their cognitive performancesPeer Reviewe

PERAN INFOGRAFIS SEBAGAI PENUNJANG DALAM PROSES PEMBELAJARAN SISWA

Easy information access supports students to find resources in learning. However, excess information caused students to face difficulty processing information well. Infographics becomes a solution to presenting the information in visual format. Infographics is expected to help students to process the information, especially for academic purposes. It is difficult to create infographics manually. This training aimed to teach students in high school to create infographics with Piktochart. Piktochart is one of the online tools to help people create infographics. The training was divided into three sessions, namely the preparation session, material introduction session, and creative session. In the last session, students created infographics from one of their subjects in school. Afterwards, the students presented their infographics to other students and trainers in class

Pelatihan Guru untuk Tantangan Bebras 2022 di Biro Bebras Universitas Kristen Maranatha

Tantangan Bebras merupakan salah satu kegiatan yang memperkenalkan computational thinking dan informatika kepada siswa sekolah. Bebras Indonesia melalui setiap mitra biro Bebras di seluruh Indonesia menyelenggarakan Tantangan Bebras setiap tahunnya yaitu pada minggu kedua bulan November. Biro Bebras Maranatha juga mempersiapkan guru-guru yang berada di bawah naungan Biro Bebras Maranatha dalam kegiatan pelatihan pada 7 Oktober 2022 secara hybrid dan technical meeting pada 28 Oktober 2022. Pelatihan untuk tahun 2022 dimulai dengan kuis soal-soal Bebras yang diambil dari soal-soal dalam Tantangan Bebras tahun-tahun sebelumnya untuk mengukur tingkat pemahaman guru dalam computational thinking. Kegiatan pelatihan dilanjutkan dengan pembahasan soal kuis melalui diskusi, penyampaian konsep computational thinking, serta pendaftaran dan persiapan siswa untuk Tantangan Bebras 2022. Pada akhir sesi pelatihan, guru-guru peserta mengisi kuesioner untuk mengetahui sejauh mana persiapan yang sudah dilakukan untuk Tantangan Bebras 2022. Pelaksanaan kegiatan dilaksanakan secara hybrid diikuti oleh 52 guru perwakilan sekolah. Dari 52 guru yang mengikuti kuis, nilai kuis berkisar antara 0 sampai 80 di mana rata-rata nilai adalah 35. Sebanyak 79% dari guru-guru yang mengikuti pelatihan ini sudah pernah mengikuti workshop Bebras di tahun-tahun sebelumnya dan 69% dari total guru tersebut telah memanfaatkan soal Bebras untuk pembelajaran di kelas. Selama proses pembekalan Tantangan Bebras, terdapat tiga tantangan terbesar yang dihadapi yaitu kemampuan berpikir siswa, persiapan guru untuk pembekalan, dan melatih siswa dalam membaca soal

Pembelajaran Computasional Thinking melalui Program Gerakan Pandai untuk Guru dan PKBM

Program Gerakan Pandai yang digagas oleh Bebras Indonesia dengan dukungan Google bertujuan untuk membuat guru mulai menjadi guru penggerak dalam menyemaikan dan menumbuh-kembangkan kemampuan Computational Thinking (CT). Melalui gerakan PANDAI ini, diharapkan guru mengenal CT dan memperkenalkan CT kepada para siswa, sehingga siswa dapat mengembangkan kemampuan  berpikir komputasional yang bersifat kritis dan kreatif. Biro Bebras Maranatha menjalankan program Gerakan Pandai dalam dua batch yang dimulai pada bulan September 2020 sampai dengan Desember 2021. Pelatihan guru  batch1 diikuti oleh 148 guru, sedangkan batch2 diikuti 394 guru. Indikator guru yang berhasil menerapkan kemampuan CT adalah guru yang melaksanakan  paling sedikit 4 sesi microteaching dalam dua semester. Guru yang tuntas melakukan microteaching untuk batch1 ada 110 orang (74%), dan batch2 ada 184 guru (47%), dengan persentase rata-rata 60.5% untuk seluruh batch.Â

Pelatihan Guru untuk Tantangan Bebras 2022 di Biro Bebras Universitas Kristen Maranatha

Tantangan Bebras merupakan salah satu kegiatan yang memperkenalkan computational thinking dan informatika kepada siswa sekolah. Bebras Indonesia melalui setiap mitra biro Bebras di seluruh Indonesia menyelenggarakan Tantangan Bebras setiap tahunnya yaitu pada minggu kedua bulan November. Biro Bebras Maranatha juga mempersiapkan guru-guru yang berada di bawah naungan Biro Bebras Maranatha dalam kegiatan pelatihan pada 7 Oktober 2022 secara hybrid dan technical meeting pada 28 Oktober 2022. Pelatihan untuk tahun 2022 dimulai dengan kuis soal-soal Bebras yang diambil dari soal-soal dalam Tantangan Bebras tahun-tahun sebelumnya untuk mengukur tingkat pemahaman guru dalam computational thinking. Kegiatan pelatihan dilanjutkan dengan pembahasan soal kuis melalui diskusi, penyampaian konsep computational thinking, serta pendaftaran dan persiapan siswa untuk Tantangan Bebras 2022. Pada akhir sesi pelatihan, guru-guru peserta mengisi kuesioner untuk mengetahui sejauh mana persiapan yang sudah dilakukan untuk Tantangan Bebras 2022. Pelaksanaan kegiatan dilaksanakan secara hybrid diikuti oleh 52 guru perwakilan sekolah. Dari 52 guru yang mengikuti kuis, nilai kuis berkisar antara 0 sampai 80 di mana rata-rata nilai adalah 35. Sebanyak 79% dari guru-guru yang mengikuti pelatihan ini sudah pernah mengikuti workshop Bebras di tahun-tahun sebelumnya dan 69% dari total guru tersebut telah memanfaatkan soal Bebras untuk pembelajaran di kelas. Selama proses pembekalan Tantangan Bebras, terdapat tiga tantangan terbesar yang dihadapi yaitu kemampuan berpikir siswa, persiapan guru untuk pembekalan, dan melatih siswa dalam membaca soal

Sistem Pendeteksi Pengirim Tweet dengan Metode Klasifikasi Naive Bayes

Until Januari 2015, social media users reached 29% of the world population. In Indonesia itself had 28% active users from total populasi of Indonesia. The usage of social media gives positives and negatives effect. The negatives effect are the increasing number of fraud by using SMS or social media, such as Twitter. Many people are deceived by the tweet messages sent from known user account when in fact the sender is other person. Because of that, there is a need to have a system to detect wheteher the tweet sender is the same person or not. Naive Bayes classifiers method is used to classify that. The data source is taken from tokens selected based on two models, the minimum n-time number of occurrences and the n-th highest number of occurrences. Each tweets also processed into six different types of tweets, such as formal tweet or lowercase tweet. The test uses tenfold cross-validation and measured by the value of accuracy, precision, recall, and F-score. The common result shows 82,145% level of accuracy. Second model to select the tokens shows consistency level of accuracy for each types of tweets. The fifth types of tweets also get the highest level of accuracy for both models to select the tokens

The Impact of Pair Programming on the Performance of Slow-Paced Students: A Study on Data Structure Courses

A study shows that pair programming can help slow-paced students in completing Introductory Programming assessment. This paper replicates the study on Data Structure course, in which the completion of the assessments does not only rely on logic but also theoretical knowledge. The aim is to check whether pair programming is still helpful on such new assessment characteristics. Three classes of Data Structure course with 14 teaching weeks and a total of 72 undergraduate students are considered in this study. Two of the classes are about Basic Data Structure while another one is the advanced one. Our evaluation shows that pair programming can help slow-paced students in both pair and individual academic performance. It also increases overall academic performance if the tasks are more logic oriented. Nevertheless, no benefits provided for fast-paced students paired to the slow-paced ones, even though all students appreciate the use of pair programming