scholarly journals EUREKATAX: A Taxonomy for the Representation and Analysis of Qualitative Usability Test Data

2020 ◽  
Vol 4 (2) ◽  
pp. 22
Author(s):  
Panagiotis Germanakos ◽  
Ludwig Fichte
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Empirical Data ◽  
Formal Method ◽  
Real Life ◽  
Learning Activity ◽  
Perceived Fit ◽  
Usability Tests ◽  
Usability Research ◽  
The One

Usability tests serve as an insightful source of feedback for product teams that want to deliver user-centered solutions and enhance the User Experience (UX) of their products and services. However, in many cases, formative usability tests in particular may generate a large volume of qualitative and unstructured data that need to be analyzed for decision making and further actions. In this paper, we discuss a more formal method of analyzing empirical data, using a taxonomy, namely Engineering Usability Research Empirical Knowledge and Artifacts Taxonomy (EUREKATAX). We describe how it can provide guidance and openness for transforming fuzzy feedback statements into actionable items. The main aim of the proposed method is to facilitate a more holistic and standardized process to empirical data analysis while adapting on the solution or context. The main contributions of this work comprise the: (a) definition of the proposed taxonomy which represents an organization of information structured in a hierarchy of four main categories (discover, learn, act, and monitor), eight sub-categories, and 52 items (actions/operations with their respective properties); (b) description of a method, that is expressed through the taxonomy, and adheres to a systematic but modular approach for analyzing data collected from the usability studies for decision making and implementation; (c) formulation of the taxonomy’s theoretical framework based on meticulously selected principles like experiential learning, activity theory: learning by expanding, and metacognition, and (d) extended evaluation into two phases, with 80 UX experts and business professionals, showing on the one hand the strong reliability of the taxonomy and high perceived fit of the items in the various classifications, and on the other hand the high perceived usability, usefulness and acceptability of the taxonomy when put into practice in real-life conditions. These findings are really encouraging, in an attempt to generate comparable, generalizable and replicable results of usability tests’ qualitative data analysis, thereby improving the UX and impact of software solutions.

scholarly journals Comparison of TIMS-PASEF quantitative proteomics data-analysis workflows using FragPipe, DIA-NN, and Spectronaut from a user's perspective

2021 ◽  
Author(s):  
Alejandro Fernandez-Vega ◽  
Federica Farabegoli ◽  
Maria Mercedes Alonso-Martinez ◽  
Ignacio Ortea
Keyword(s):  
Data Analysis ◽  
Quantitative Proteomics ◽  
Real Life ◽  
Analysis Tool ◽  
Proteomics Data ◽  
Mass Spectrometers ◽  
Data Independent Acquisition ◽  
Proteomics Experiment ◽  
Under Sampling ◽  
The One

Data-independent acquisition (DIA) methods have gained great popularity in bottom-up quantitative proteomics, as they overcome the irreproducibility and under-sampling limitations of data-dependent acquisition (DDA). diaPASEF, recently developed for the timsTOF Pro mass spectrometers, has brought improvements to DIA, providing additional ion separation (in the ion mobility dimension) and increasing sensitivity. Several studies have benchmarked different workflows for DIA quantitative proteomics, but mostly using instruments from Sciex and Thermo, and therefore, the results are not extrapolable to diaPASEF data. In this work, using a real-life sample set like the one that can be found in any proteomics experiment, we compared the results of analyzing PASEF data with different combinations of library-based and library-free analysis, combining the tools of the FragPipe suite, DIA-NN and including MS1-level LFQ with DDA-PASEF data, and also comparing with the workflows possible in Spectronaut. We verified that library-independent workflows, not so efficient not so long ago, have greatly improved in the recent versions of the software tools, and now perform as well or even better than library-based ones. We report here information so that the user who is going to conduct a relative quantitative proteomics study using a timsTOF Pro mass spectrometer can make an informed decision on how to acquire (diaPASEF for DIA analysis, or DDA-PASEF for MS1-level LFQ) the samples, and what can be expected depending on the data analysis tool used, among the different alternatives offered by the recently optimized tools for TIMS-PASEF data analysis.

scholarly journals Dynamic setting of shipping points in logistics systems with multiple heterogeneous warehousesorecasting the confidence interval of efficiency in fuzzy DEA

2021 ◽  
Vol 31 (2) ◽  
Author(s):  
Dobromir Herzog
Keyword(s):  
Decision Making ◽  
Confidence Interval ◽  
Planning Process ◽  
Real Life ◽  
Logistics Systems ◽  
Geographically Dispersed ◽  
Dynamic Setting ◽  
The One ◽  
Fuzzy Dea ◽  
Better Than

This study aims to model a planning process of handling outbound deliveries from a set of geographically dispersed warehouses. The model incorporates parameters observed in real life supply chains and allows simulation of various variants of process supporting decision making of current shipment management, as well as strategic planning of distribution network. A heuristic algorithm that can be used for planning of source warehouses for shipments is proposed. The model is built and tested on real business data and its performance proves to be better than the one currently used by reference company.

Process tracing

KWALON ◽  
2014 ◽  
Vol 19 (3) ◽  
Author(s):  
Kim Loyens
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Empirical Data ◽  
Case Analysis ◽  
Process Tracing ◽  
Fine Grained ◽  
Step Model ◽  
Dependent Variables ◽  
The Relationship ◽  
Structured Approach

Process tracing: a structured approach to data analysis in decision making studies Process tracing: a structured approach to data analysis in decision making studies Process tracing (PT) has been developed for within-case analysis to unravel causal mechanisms that explain the relationship between independent and dependent variables. It follows an iterative-inductive approach to generate theory on the basis of empirical data. This paper explains how PT can be used in decision making research by using a three step model: (1) one code for each case, (2) preparation of an analytic table, and (3) from fragmentation to integration. The cases that illustrate these steps show that PT results in fine-grained theoretical explanations for decision making and indicates the need of additional between-case analysis.

Vehicular Traffic Forecasting in Filling Station

2017 ◽  
pp. 243-262
Author(s):  
Peeyush Pandey ◽  
Tuhin Sengupta
Keyword(s):  
Decision Making ◽  
Real Life ◽  
Future Trend ◽  
Decision Making Process ◽  
Clear Understanding ◽  
Data Set ◽  
Traffic Forecasting ◽  
Single Data ◽  
The One ◽  
Forecasting Techniques

Forecasting is the one of the important part of decision making process. It helps managers to identify short term and long term future trends in the business activities. It may help in forecasting demand in retail store, predicting customer traffic at the petrol pump, calculation of probable population in upcoming years etc. There are plenty of studies published on forecasting techniques which are just introductory or highly mathematical and lacks in providing managerial perspective of solving business problems to the students. This chapter elucidates various forecasting techniques and its application in the field of management. In addition, various examples of real life problems are solved and analyzed with multiple forecasting techniques. Through this chapter students will have a clear understanding of the various nuances of different forecasting models in one single data set. Students will be able to identify future trend and seasonality in real life data set and evaluate more appropriate forecasting technique for the decision-making process.

Medical Diagnosis: A Real Life Application of Logic

1978 ◽  
Vol 71 (6) ◽  
pp. 499-504
Author(s):  
Kenneth P. Goldberg
Keyword(s):  
Decision Making ◽  
Mathematical Logic ◽  
Medical Diagnosis ◽  
Real Life ◽  
Junior College ◽  
The Other ◽  
College Level ◽  
Human Reason ◽  
The One ◽  
Rational Reasoning

Of all the mathematical topics taught at the high school and junior college level, logic must certainly be one of the most frustrating for both students and teachers. On the one hand, the student is told that logic is the necessary basis on which all rational reasoning and decision making rests. On the other hand, since few situations involving human reason are simple enough to permit the application of mathematical logic, it is extremely difficult to discuss and illustrate the use of logic in a believable and interesting manner.

Medical Diagnosis from a Clinician’s Point of View

1978 ◽  
Vol 17 (01) ◽  
pp. 28-35
Author(s):  
F. T. De Dombal
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Computer Science ◽  
Medical Diagnosis ◽  
Information Science ◽  
Point Of View ◽  
Joint Research ◽  
The World

This paper discusses medical diagnosis from the clinicians point of view. The aim of the paper is to identify areas where computer science and information science may be of help to the practising clinician. Collection of data, analysis, and decision-making are discussed in turn. Finally, some specific recommendations are made for further joint research on the basis of experience around the world to date.

Analisis Motivasi Belajar Siswa SMP Dalam Pembalajaran Outdoor-Modeling Mathematics Berdasarkan Gaya Belajar

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Sofnidar ◽  
Hartina ◽  
Kamid ◽  
Khairul Anwar
Keyword(s):  
Student Behavior ◽  
Mathematics Learning ◽  
Real Life ◽  
Data Retrieval ◽  
Learning Activities ◽  
Learning Activity ◽  
Stimulus Selection ◽  
Learning Behavior ◽  
Research Subjects ◽  
Motor Activities

Prilaku belajar adalah suatu sikap y ang muncul dari diri siswa dalam menanggapi dan meresponi setiap kegiatan belajar mengajar yang terjadi. salah satu wujud dari prilaku adalah motivasi belajar. Menurut teori behavioristik, belajar adalah perubahan tingkah laku sebagai akibat adanya interaksi antara stimulus (rangsangan) dan respon (tanggapan). Stimulus yang diberikan guru dalam pembelajaran tertuang dalam rancangan aktifitas pembelajaran. Aktivitas pembelajaran merupakan kegiatan yang dirancang guru untuk mewujudkan dan atau menciptkan kondisi belajar siswa (stimulus). Pemilihan aktivitas belajar yang sesuai memungkinkan untuk terjadinya efektivitas pedagogis dalam mencapai tujuan pembelajaran, maupun dapat membentuk prilaku positif siswa (respon) dalam belajar. Desain pembelajaran berbasis outdoor-medelling mathematics memuat serangkain aktivitas kegiatan pembelajaran yang berbassis investigasi konteks masalah outdoor (masalah real life) dengan muatan konten materi modeling mathematics. Pada makalah ini akan membahas prilaku belajar dan bagaimana motivasi terbentuk melaui aktifitas kegiatan pebelajaran outdoor-medelling mathematics yang diklasifikasikan menjadi motoractivities mentalactivities, visualactivities, emotionalactivities, motoractivitie.Melalui metode kulitatif deskriptif, dengan mengambil 20 siswa kelas IX-B SMP N 1 Muaro Jambi yang mempunyai gaya belajar visual, auditorial, dan kinestetik. Setelah pelaksanaan pembelajaran, pengambilan data dilakukan melalui angket, dan lembar pengamatan beserta wawancara ke subjek penelitian. Hasil penelitian menunjukan bahwa aktivitas belajar dalam pembelajaran yang dapat memotivasi siswa belajar matematika adalah visualactivities sebesar 74,16%; motoractivities sebesar 96,67%; mentalactivities sebesar 71,66%; dan emotionalactivities sebesar 73,33%. Berdasarkan hasil analisis yang dilakukan aktivitas belajar dalam pembelajaran outdoor-medelling mathematics matematika yang paling dominan dapat memotivasi siswa belajar adalah motoractivities dengan persentasi 96,67% dengan kriteria sangat baik dan sangat memotivasi siswa belajar matematika dalam pembelajaran luar kelas. Indikatornya adalah melakukan percobaan. Kelebihan aktivitas belajar dalam pembelajaran outdoor-medelling mathematics adalah, aktivitas belajar lebih membuat siswa termotivasi untuk belajar matematika. Siswa menjadi lebih aktif dan interaksi dengan teman sesamanya semakin meningkat juga. Adapun kelemahan aktivitas belajar dalam pembelajaran luar kelas adalah sulit untuk siswa terfokus dalam aktivitas belajar yang sedang dilakukan.   Learning behavior is an attitude that arises from students in responding and responding to each teaching and learning activity that occurs. one form of behavior is learning motivation. According to behavioristic theory, learning is a change in behavior as a result of an interaction between stimulus (stimulus) and response (response). The stimulus given by the teacher in learning is contained in the design of learning activities. Learning activities are activities designed by the teacher to realize and or create the conditions for student learning (stimulus). Selection of appropriate learning activities allows for the occurrence of pedagogical effectiveness in achieving learning goals, and can form positive student behavior (response) in learning. Outdoor-based learning mathematics learning design contains a series of learning activities based on the context of outdoor problems (real life problems) with the content of modeling mathematics material. In this paper will discuss learning behavior and how motivation is formed through the activities of learning activities outdoor-modeling mathematics which are classified into mental activities, visual activities, emotional activities, motor activities. Through the descriptive qualitative method, taking 20 students of class IX-B Muaro Jambi Middle School 1 who have visual, auditory, and kinesthetic learning styles. after the implementation of learning, data retrieval was carried out through questionnaires, and observation sheets and interviews to the research subjects. The results showed that learning activities in learning that could motivate students to learn mathematics were visual activities at 74.16%, motor activities at 96.67%, mental activities at 71.66%, and emotional activities at 73.33 %%. Based on the results of the analysis carried out learning activities in mathematics outdoor-modeling mathematics learning the most dominant motivating students to learn is motor activities with a percentage of 96.67% with very good criteria and very motivating students to learn mathematics in learning outside the classroom. The indicator is to experiment. The advantages of learning activities in outdoor-modeling mathematics learning are that learning activities make students more motivated to learn mathematics. Students become more active and interactions with their peers also increase. The weaknesses of learning activities in learning outside the classroom is difficult for students to focus on the learning activities that are being done.

THE EFFECT BETWEEN COOPERATIVE LERANING AND LEARNING STYLE OF THE GERMANY WRITTING OUTCOM

Jurnal KATA ◽  
2017 ◽  
Vol 1 (2) ◽  
pp. 172
Author(s):  
Fauzan Adhima
Keyword(s):  
Data Analysis ◽  
Cooperative Learning ◽  
Learning Styles ◽  
Factorial Design ◽  
Learning Style ◽  
Interaction Effect ◽  
Empirical Data ◽  
Visual Learning ◽  
Design Data ◽  
Auditory Learning

<p><em>The aim of the study is to reveal the empirical data about the effect of cooperative learning and learning styles on the outcome of germany writting of SMA Negeri 42 Jakarta. The methodology used is the experimental method with a 2x2 factorial design. Data analysis performed by using two way variance analysis. The findings of the study demonstrated: 1)   The teaching cooperative leraning type STAD was higher than students who take cooperative leraning type Pair Cheks teaching. 2) The visual learning style was higher than students who have auditory learning style. 3) The students who take the teaching  cooperative leraning type Pair Cheks and have a visual learning style higher than students who take cooperative leraning type STAD and have a visual learning style . 4) The cooperative leraning type STAD and auditory learning style is higher than  cooperative leraning type Pair Cheks and have auditory learning styles. 5) The teaching cooperative leraning type STAD and have a auditory learning style was higher than the teaching cooperative leraning type STAD and have visual learning styles. 6) The cooperative leraning type Pair Cheks and have a visual learning style is higher than cooperative leraning type Pair Cheks and have auditory learning styles. 7) There is an interaction effect between  cooperative leraning  and learning style of the germany writting outcome.</em></p><p><em><br /></em></p><p> </p><p>Tujuan dari penelitian ini adalah untuk mengetahui pengaruh pembelajaran kooperatif (tipe STAD dan tipe <em>Pair Cheks</em>) dan gaya belajar (visual dan auditori) terhadap keterampilan menulis bahasa Jerman Siswa SMA N 42 Jakarta. Penelitian ini merupakan penelitian kuantitatif dengan metode eksperimen menggunakan desain faktorial 2 X 2. Pengukuran keterampilan menulis bahasa Jerman menggunakan tes tulis sedangkan gaya belajar menggunakan kuesioner. Teknik analisis data dalam penelitian ini adalah teknkik analisis variansi (ANAVA) 2 jalur dan dilanjutkan dengan uji Tuckey untuk melihat interaksi antar kelompok.  Hasil penelitian menunjukan bahwa: (1) keterampilan menulis tipe STAD lebih baik dari pada tipe <em>Pair Cheks</em>, (2) gaya belajar visual lebih baik dari auditori, (3) gaya belajar auditori lebih baik menggunakan pembelajaran kooperatif tipe STAD dari pada tipe <em>Pair Cheks</em>, (4) keterampilan menulis bahasa Jerman kelompok siswa yang memiliki gaya belajar visual lebih baik menggunakan pembelajaran kooperatif tipe <em>Pair Cheks</em> dari pada tipe STAD, (5) Belajar dengan pembelajaran kooperatif tipe STAD lebih baik diterapkan pada siswa yang memiliki gaya belajar auditori dari pada gaya belajar visual, (6) belajar dengan pembelajaran kooperatif tipe <em>Pair Cheks</em> lebih baik diterapkan pada siswa yang memiliki gaya belajar visual dari pada auditori, dan (7) terdapat pengaruh interaksi antara pembelajaran kooperatif dan gaya belajar terhadap keterampilan menulis bahasa Jerman.</p><p>Tujuan dari penelitian ini adalah untuk mengetahui pengaruh pembelajaran kooperatif (tipe STAD dan tipe <em>Pair Cheks</em>) dan gaya belajar (visual dan auditori) terhadap keterampilan menulis bahasa Jerman Siswa SMA N 42 Jakarta. Penelitian ini merupakan penelitian kuantitatif dengan metode eksperimen menggunakan desain faktorial 2 X 2. Pengukuran keterampilan menulis bahasa Jerman menggunakan tes tulis sedangkan gaya belajar menggunakan kuesioner. Teknik analisis data dalam penelitian ini adalah teknkik analisis variansi (ANAVA) 2 jalur dan dilanjutkan dengan uji Tuckey untuk melihat interaksi antar kelompok.  Hasil penelitian menunjukan bahwa: (1) keterampilan menulis tipe STAD lebih baik dari pada tipe <em>Pair Cheks</em>, (2) gaya belajar visual lebih baik dari auditori, (3) gaya belajar auditori lebih baik menggunakan pembelajaran kooperatif tipe STAD dari pada tipe <em>Pair Cheks</em>, (4) keterampilan menulis bahasa Jerman kelompok siswa yang memiliki gaya belajar visual lebih baik menggunakan pembelajaran kooperatif tipe <em>Pair Cheks</em> dari pada tipe STAD, (5) Belajar dengan pembelajaran kooperatif tipe STAD lebih baik diterapkan pada siswa yang memiliki gaya belajar auditori dari pada gaya belajar visual, (6) belajar dengan pembelajaran kooperatif tipe <em>Pair Cheks</em> lebih baik diterapkan pada siswa yang memiliki gaya belajar visual dari pada auditori, dan (7) terdapat pengaruh interaksi antara pembelajaran kooperatif dan gaya belajar terhadap keterampilan menulis bahasa Jerman.</p>

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