Cookin\u27 at the Cookery

Program for the 2020 production of Cookin\u27 at the Cookeryhttps://digitalcommons.daemen.edu/musicalfare_programs/1161/thumbnail.jp

PRET: Prerequisite-enriched terminology. A case study on educational texts

In this paper we present PRET, a gold dataset annotated for prerequisite relations between educational concepts extracted from a computer science textbook, and we describe the language and domain independent approach for the creation of the resource. Additionally, we have created an annotation tool to support, validate and analyze the annotation

Towards Portability of Models for Predicting Students’ Final Performance in University Courses Starting from Moodle Logs

Predicting students’ academic performance is one of the older challenges faced by the educational scientific community. However, most of the research carried out in this area has focused on obtaining the best accuracy models for their specific single courses and only a few works have tried to discover under which circumstances a prediction model built on a source course can be used in other different but similar courses. Our motivation in this work is to study the portability of models obtained directly from Moodle logs of 24 university courses. The proposed method intends to check if grouping similar courses by the degree or the similar level of usage of activities provided by the Moodle logs, and if the use of numerical or categorical attributes affect in the portability of the prediction models. We have carried out two experiments by executing the well-known classification algorithm over all the datasets of the courses in order to obtain decision tree models and to test their portability to the other courses by comparing the obtained accuracy and loss of accuracy evaluation measures. The results obtained show that it is only feasible to directly transfer predictive models or apply them to different courses with an acceptable accuracy and without losing portability under some circumstances

Predictive Analysis of Students’ Learning Performance Using Data Mining Techniques: A Comparative Study of Feature Selection Methods

The utilization of data mining techniques for the prompt prediction of academic success has gained significant importance in the current era. There is an increasing interest in utilizing these methodologies to forecast the academic performance of students, thereby facilitating educators to intervene and furnish suitable assistance when required. The purpose of this study was to determine the optimal methods for feature engineering and selection in the context of regression and classification tasks. This study compared the Boruta algorithm and Lasso regression for regression, and Recursive Feature Elimination (RFE) and Random Forest Importance (RFI) for classification. According to the findings, Gradient Boost for the regression part of this study had the least Mean Absolute Error (MAE) and Root-Mean-Square Error (RMSE) of 12.93 and 18.28, respectively, in the case of the Boruta selection method. In contrast, RFI was found to be the superior classification method, yielding an accuracy rate of 78% in the classification part. This research emphasized the significance of employing appropriate feature engineering and selection methodologies to enhance the efficacy of machine learning algorithms. Using a diverse set of machine learning techniques, this study analyzed the OULA dataset, focusing on both feature engineering and selection. Our approach was to systematically compare the performance of different models, leading to insights about the most effective strategies for predicting student success

Model Prediksi dengan Pembelajaran Mesin dalam Pemberian Program Beasiswa kepada Calon Mahasiswa Baru Program S1 di Perguruan Tinggi Swasta.

Competition in the higher education, especially private higher education (PTS) in the digital era, is becoming increasingly tough. In order to achieve the number of prospective new students, various methods are used so that the target for admitting the number of new students can be achieved in each new academic year. Providing a scholarship program is one way to attract the prospective new students. The awarding of a scholarship program must consider various possibilities such as the seriousness or commitment of the prospective new student. Refusal to grant scholarship programs can occur and become an obstacle for achieving the target. The prediction model through machine learning using some variables such as high school’s name, high school “category”, province or area of high school located, focus of specialization in high school, high school’s grade, type of parents income, and selected major of study in higher education. All of those variables will provides the probability values that will become an indicator that can be used to prioritize requests for scholarship program applications by taking into account the factors of acceptance or rejection from prospective students. Currently there is no measurement with accuracy of acceptance or rejection from prospective students. The purpose of this research is to build and compare machine learning models such as Logistic Regression, Artificial Neural Networks, Support Vector Machines, Decision Trees, Naïve Bayes, and K Nearest Neighbors so that a machine learning model is obtained that has the best predictions for awarding scholarship programs. The result of this research is that the Logistic Regression model has the highest model average accuracy value (62,05%) from training data compared to others. The highest accuracy of Logistic Regression model (62,29%) achieved based on the testing data. The highest AUC value (0,818) generated by Logistic Regression model which means the model is able to do the classification categorized “Good Classification” compare to other models.Persaingan di dalam dunia pendidikan tinggi secara khusus Perguruan Tinggi Swasta (PTS) terutama di era digital menjadi semakin ketat. Dalam memperebutkan jumlah calon mahasiswa baru yang tersedia, berbagai cara dilakukan agar target penerimaan jumlah mahasiswa baru dapat tercapai. Pemberian program beasiswa adalah salah satu cara menjaring calon mahasiswa baru. Pemberian program beasiswa harus mempertimbangkan berbagai kemungkinan seperti keseriusan atau komitmen sedangkan penolakan pemberian program beasiswa dapat juga terjadi dan menjadi kendala pada akhir suatu periode Penerimaan Mahasiswa Baru (PMB). Model prediksi melalui pembelajaran mesin dengan beberapa atribut seperti asal sekolah SMA, “Kategori Sekolah” SMA, provinsi atau daerah asal SMA, jurusan saat SMA yang diambil, nilai akademik SMA, jenis pekerjaan orang tua, dan pilihan program studi atau jurusan yang akan diambil saat nanti berkuliah pada akhirnya dapat memberikan suatu indikator nilai peluang atau kemungkinan penerimaan atau penolakan program beasiswa dari seorang calon mahasiswa baru. Saat ini belum ada usaha untuk memprediksi secara sistematis terhadap penerimaan / penolakan program beasiswa. Tujuan penelitian ini adalah membangun dan membandingkan model pembelajaran mesin seperti Logistic Regression, Artificial Neural Network, Support Vector Machine, Decision Tree, Naïve Bayes, dan K Nearest Neighbors sehingga didapatkan satu model pembelajaran mesin yang memiliki prediksi yang terbaik terhadap pemberian program beasiswa. Dari hasil penelitian maka model Logistic Regression memiliki nilai akurasi rata-rata tertinggi (62,05%) saat melakukan pembelajaran model dengan data latihan dibandingkan dengan model lainnya. Akurasi model Logistic Regression memiliki nilai tertinggi terhadap data uji sebesar (62,29%) dan juga memiliki nilai AUC (0.818) yang berarti bahwa model dapat melakukan pengklasifikasian dengan baik terhadap kelompok pengambilan keputusan dibandingkan dengan model lainnya

Understanding modes of dwelling: A transdisciplinary approach to phenomenology of landscape

This transdisciplinary PhD addresses the research question: Can some form of phenomenology provide an effective over-arching paradigm for transdisciplinary research in ethnophysiography? Ethnophysiography studies the way people within a language community conceptualise natural landscape, including terms for landscape features and toponyms (placenames). Dwelling involves conceptualisations and affects regarding physical, utilitarian, cultural, spiritual and ethical relationships with landscape. A key achievement is development of an enhanced ethnophysiography case study methodology, supporting the Ethnophysiography Descriptive Model (EDM). Summary phenomenographic tables were prepared from literature reviews of ethnophysiography, transdisciplinarity, phenomenology, concepts of place and relationships with place. The use of tables, summarising key results of literature reviews (via a phenomenographic approach), is integral to the methodology, to operationalize transdisciplinarity. Some tables are utilised in the PTM-ECS, facilitating identification of relevant issues, collection of appropriate data, and hermeneutic analysis processes. To facilitate comparison of landscape terms and toponyms between languages, the EDM was developed and tested. A key contribution is interpretation of the phenomenological concepts of ‘lifeworld’, ‘topology’ and ‘habitus’. Creation of landscape, as place, involves synergistic integration, in a non-deterministic and emergent manner, of the physical attributes of an area of topographic environment (terrain and ecosystem) with the socio-cultural characteristics of a group of people (including linguistic and spiritual aspects). This produces a particular topo-socio-cultural-spiritual mode-of-dwelling (topology). A partial trial of the new methodology is provided, via an ethnophysiography case study with Manyjilyjarra Aboriginal people in Australia’s Western Desert (undertaken by this author with linguist Clair Hill). It demonstrates how the adopted approach facilitates understanding of traditional forms of dwelling and how this relates to Jukurrpa (The Dreaming), the law, lore and social structure of their society. Review of research processes indicates they effectively utilised key features of transdisciplinarity. A summary of the findings, their potential application, a statement of research limitations, and proposals for further research, are provided