Analyzing Sequence Data with Markov Chain Models in Scientific Experiments

Paxinou, Evgenia; Kalles, Dimitris; Panagiotakopoulos, Chris; Verykios, Vassilios S.

doi:10.1007/s42979-021-00768-5

Cited by 13 publications

(4 citation statements)

References 60 publications

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“…The Markov model is a probabilistic model that handles sequential data by assuming that each observation is dependent solely on the current state of a discrete variable that evolves over time (i.e., as a Markov chain 41 ). The Markov model is characterized by the number of states and transition probabilities, which determine the likelihood of transitioning from one state to another.…”

Section: Methodsmentioning

confidence: 99%

Toddlers strategically adapt their information search

Poli,

Li,

Naidu

et al. 2024

Nat Commun

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Adaptive information seeking is essential for humans to effectively navigate complex and dynamic environments. Here, we developed a gaze-contingent eye-tracking paradigm to examine the early emergence of adaptive information-seeking. Toddlers (N = 60, 18-36 months) and adults (N = 42) either learnt that an animal was equally likely to be found in any of four available locations, or that it was most likely to be found in one particular location. Afterwards, they were given control of a torchlight, which they could move with their eyes to explore the otherwise pitch-black task environment. Eye-movement data and Markov models show that, from 24 months of age, toddlers become more exploratory than adults, and start adapting their exploratory strategies to the information structure of the task. These results show that toddlers’ search strategies are more sophisticated than previously thought, and identify the unique features that distinguish their information search from adults’.

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Section: Methodsmentioning

confidence: 99%

Toddlers strategically adapt their information search

Poli,

Li,

Naidu

et al. 2024

Nat Commun

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“…Також, в процесі отримання вмінь, на базі практичного використання систем імітаційного моделювання та емуляції, учасниками навчального процесу використовується вбудований модуль тестування із завданнями різних рівнів складності. При тестуванні студенти перевіряють параметри налаштування мережевих пристроїв, доступність кінцевих вузлів та наявність зв'язків між сукупністю сегментів складної мережі [12]. Такий підхід сприяє не формальному підходу до виконання завдань, так як студенти не прив'язані до звичайних послідовних дій (рис.…”

Section: рис 3 модель отримання знань та вміньunclassified

Model of Improving Practical Skills in Computer Engineering

Kyrychek

2024

Traditions and New Scientific Strategies in the Context of Global Transformation of Society

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The paper proposes a multi-level approach to teaching network disciplines using network modeling and emulation systems from the standpoint of learning and testing knowledge. The application of the modular principle allows you to create models of maximally adequate networks and obtain more reliable results. The purpose of the study is to establish the connection between the introduction of simulation modeling systems and emulation of network objects into the educational process and improving the quality of knowledge acquisition and obtaining practical skills. The object of the research is the process of building a model of obtaining practical skills on the example of studying network disciplines. The subject is models, methods and software tools for improving the quality of acquiring knowledge and skills in the learning process. The main tasks are the adaptation of the participants of the educational process to the growing flow of knowledge, free orientation in the arrays of knowledge, application of specialized systems to improve practical skills, and the ability to quickly find and use all available resources. Acquiring practical skills in the training of highly qualified specialists in computer engineering is an important direction of using modeling systems, emulation and design in order to increase the effectiveness of training, as well as the application of calculations in modeling the operation of real objects. Other methods of learning, which are based on visualization of the functionality of the object being studied, can guarantee better assimilation of theoretical knowledge and improvement of the level of practical skills when studying technologies of data transmission systems. Implementation methods of modeling the working of objects, using intelligent calculations, understanding physical processes in the environment of transmission and use of artificial intelligence, improve the quality of acquired knowledge related to the design and implementation of data transmission systems for different purposes. The author considered the time distribution regularities for the assimilation of knowledge by the participants of the educational process. It is shown that the acquisition of knowledge and skills is quite well described by the exponential law for small volumes and the gamma distribution for masses of knowledge. The examples show the confirmation of theoretical assumptions by experimental data. The stages of combining the model of the learning process for obtaining knowledge with the process of improving its quality and obtaining practical skills when applying simulation modeling systems are defined.

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“…Zammarchi, Frigau and Mola evaluated the web usability of a University in Italy website using eye tracking data, utilizing Markov chain analysis to study transitions between different areas of interest and suggesting areas for improvement in web usability based on fixation counts and the Markov chain analysis [38]. Paxinou et al investigated how virtual simulations enhance learning outcomes in science labs in a Greek University using a Markov Chain Model to predict students' performance, proposing that learners trained with virtual reality exhibit higher proficiency in conducting experiments compared to traditional methods [39]. Fraoua and David optimized the learning path in an online course using a Markov Chain Model, observing that learners were bored and as a result they dropped out of their courses [40].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Tracing Student Activity Patterns in E-Learning Environments: Insights into Academic Performance

Paxinou,

Feretzakis,

Tsoni

et al. 2024

Future Internet

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In distance learning educational environments like Moodle, students interact with their tutors, their peers, and the provided educational material through various means. Due to advancements in learning analytics, students’ transitions within Moodle generate digital trace data that outline learners’ self-directed learning paths and reveal information about their academic behavior within a course. These learning paths can be depicted as sequences of transitions between various states, such as completing quizzes, submitting assignments, downloading files, and participating in forum discussions, among others. Considering that a specific learning path summarizes the students’ trajectory in a course during an academic year, we analyzed data on students’ actions extracted from Moodle logs to investigate how the distribution of user actions within different Moodle resources can impact academic achievements. Our analysis was conducted using a Markov Chain Model, whereby transition matrices were constructed to identify steady states, and eigenvectors were calculated. Correlations were explored between specific states in users’ eigenvectors and their final grades, which were used as a proxy of academic performance. Our findings offer valuable insights into the relationship between student actions, link weight vectors, and academic performance, in an attempt to optimize students’ learning paths, tutors’ guidance, and course structures in the Moodle environment.

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Analyzing Sequence Data with Markov Chain Models in Scientific Experiments

Cited by 13 publications

References 60 publications

Toddlers strategically adapt their information search

Toddlers strategically adapt their information search

Model of Improving Practical Skills in Computer Engineering

Tracing Student Activity Patterns in E-Learning Environments: Insights into Academic Performance

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