At present, a new generation of students is studying at universities with their specific abilities and skills. In addition to hard skills, potential employers also require significantly developed soft skills from their employees. The school, and not just the university, is entering this process in a different position than in the past due to the changing requirements placed on it. Activities in the school environment must adapt to emerging trends. This article explains how present-day requirements on graduates and specifics of the new generation of students challenge the approach to education. In order to increase the efficiency and attractiveness of the educational process, the SCRUM framework has been implemented into course Mathematics 1. The re-designed course was implemented and validated by an experimental group of students in the academic year 2019/2020. Two questionnaire analyses and a comparison of learning outcomes were conducted to find out the answers to research questions. The results of students indicate that agile methodologies are useful for increasing the efficiency of the educational process. The paper offers our proposal for the introduction of agile methodology into the educational process and our first-hand experience with this way of teaching and its impact on the studying results of the experimental group and, last but not least, suggestions for possible improvements.

This study aims to highlight the importance of a systematic approach to process capability assessment and the importance of following a sequence of steps. Statistical process control provides several different ways of assessing process capability. This study evaluates the process capability of crown cap manufacturing through capability indices. In addition to calculating the indices, the evaluation involves extensive data analysis. Before calculating the capability indices, the assumptions for their correct selection and use were also verified. Several statistical tests were used to verify each assumption. The research value of the study lies in pointing out that not all tests led to the same conclusions. It highlights the importance of selecting the appropriate test type for the evaluated process quality characteristics.

In general, college students have concerns about mastering the Statistics course. Several scientific articles suggest that methodologies in which the student is an active part of the educational process lead to their greater involvement in teaching activities and better results in their overall assessment. A modified form of the Mathematics course inspires this study. The Mathematics course precedes the Statistics course. This study presents students’ results from both affected courses for the last 12 years. The authors obtained the data from the Modular Academic Information System (MAIS). The study describes the Statistics course, which is a combination of the traditional form of teaching and learning with elements of active learning. The product is a redesign of this course. The proposed changes are based on the requirements of some stakeholders. The results are from a survey of course graduates, from the experience of online teaching during the COVID-19 pandemic, and the introduction of active-learning elements. The authors of the article point out the risks and opportunities they found in teaching the Statistics course.

The paper proposes a rock classification method using the vibration signal of the rotary drilling process. This proposed method classifies four rocks with a reliability of 100 %, from the vibration signal record of a lasting 1/4 second. For the design of a suitable classification method, several attributes of the vibration signal were calculated for two different signal recording lengths. Cluster dendrogram, ANOVA test, and boxplot were used to determine useful attributes and proper signal length. The classification rule was found using a decision tree, machine learning tool. The publication gradually describes the process of creating the classification method and the results of the reliability verification of the proposed classification method. Rock disintegration by rotary drilling was carried out under standardized experimental conditions. The disintegration rocks were andesite, granite, limestone, and concrete.

Technical and technological developments in recent decades have stimulated the rapid development of methods and tools in the field of statistical process quality control, which also includes control charts. The principle of control charts defined by Dr. W. Shewhart has been known for more than 100 years. Since then, they have been used in many industries to monitor and control processes. This paper aims to assess the possibilities of use and the selection of the most suitable type of control chart for monitoring the quality of a process depending on its nature. This tool should help operators in monitoring coking time, which is one of the important control variables affecting the quality of coke production. The autoregressive nature of the variable being monitored was considered when selecting a suitable control chart from the group of options considered. In addition to the three traditional types of control charts (Shewhart’s, CUSUM, and EWMA), which were applied to the residuals of individual values of different types of ARIMA models, various statistical tests, and plots, a dynamic EWMA control chart was also used. Its advantage over traditional control charts applied to residuals is that it works with directly measured coking time data. This chart is intended to serve as a method to monitor the process. Its role is only to alert the process operator to the occurrence of problems with the length of the coking time.

This research aims to classify rock types based on the vibration signal propagated from the experimental rotary drilling process, where the generated vibration signal is a source of information. Its measurement and processing provide important information about the rock disintegration process, the drilled rock, the drilling tool, and the drilling parameters. For the design of a suitable classification method, several attributes of the vibration signal were calculated for two different signal recording lengths. A cluster dendrogram, an ANOVA test, and a boxplot were used to determine attributes and proper signal length. The classification rule was found using a decision tree, a machine-learning tool. This publication gradually describes the process of creating the classification method and the results of the reliability verification of the proposed classification method. The disintegrated rocks were andesite, granite, limestone, and concrete used as artificial rock. This proposed method classified these three rock types and concrete with a reliability of 100% from a vibration signal record lasting 1/4 s.