A Decision-Making Model for Teaching Higher Order Thinking Skills (HOTS) to College Students

Sella, Ana Carolina; Santos, Caue Bittencourt Angelo dos; Santos, Isvania Alves dos; Santos, Jackeline Joyce de Santana; Santana, Lucas Tadeu Vieira Moura de

doi:10.1109/icalt.2019.00087

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…The primary objective of incorporating technology into the learning model is to facilitate learners in developing critical thinking, innovation, communication skills, teamwork, computer literacy, and collaboration [1]. Critical thinking skills are a component of Higher Order Thinking Skills (HOTS), which encompass problemsolving abilities [2]. Enhancing proficiency in HOTS (Higher Order Thinking Skills) and problem-solving abilities can be achieved by employing software simulation as a viable strategy.…”

Section: Introductionmentioning

confidence: 99%

The Efficacy of the MOOC+SPOC Synchronous Learning Model in Vocational Education for Process Control Systems

Utami,

Yahya,

Purnamawati

2024

AJESS

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Incorporating the Industrial Revolution into vocational education, particularly in enhancing curriculum and learning, encounters numerous obstacles. Utilizing technology in interactive learning is anticipated to actively involve learners and enhance their abilities in critical thinking, creativity, innovation, and problem-solving. This study emphasizes the limitations of traditional learning approaches in Process Control Systems and presents a novel framework of MOOC+SPOC Synchronous using a Research and Development (R&D) methodology. The Process Control System course encounters impediments that negatively impact the graduation rate, which is suboptimal. The merging of MOOC (Massive Open Online Course) and SPOC (Small Private Open Course) models into a synchronous MOOC+SPOC learning model seeks to enhance learning outcomes through the utilization of a cloud computing-based Virtual Remote Laboratory (VRLab). The outcome is a model that has been rigorously evaluated and proven effective through empirical research. The research data demonstrated this model's statistically significant positive effect on enhancing student learning outcomes. The Pre-Test and Post-Test results revealed that the experimental group experienced a notable improvement in learning outcomes, with an increase ranging from 10% to 46%. In comparison, the control group also exhibited increased learning outcomes, ranging from 5% to 39%. The statistical analysis reveals a consistent and significant growth in the Experimental group, indicating that students exhibit a favorable level of motivation throughout the learning process, as indicated by the average significance value exceeding 0.5. Based on the test findings acquired from this study, it can be concluded that the Synchronous MOOC+SPOC model is a viable alternative for addressing future difficulties in vocational education. This model is effective and has been proven to work.

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Section: Introductionmentioning

confidence: 99%

The Efficacy of the MOOC+SPOC Synchronous Learning Model in Vocational Education for Process Control Systems

Utami,

Yahya,

Purnamawati

2024

AJESS

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“…With the shift of global education view to humanism, more and more scholars and experts have proposed that recessive quality is the key support for students to achieve success in the long run, and gradually deepened their research on the sustainable development ability of students in higher vocational colleges [11][12][13][14][15]. However, the existing relevant research lacks a unified standard, which cannot give a targeted and feasible overview and description of the sustainable development ability [16][17][18][19][20], because the core and basic point of sustainable development of students is to develop their personality and teach them according to their aptitude.…”

Section: Introductionmentioning

confidence: 99%

Teaching Decision Optimization for Sustainable Development of Students

Kang¹,

Gao²,

Liang³

2023

Int. J. Emerg. Technol. Learn.

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With the shift of the global education view to humanism, more and more scholars and experts have gradually deepened their research on sustainable development ability of students in higher vocational colleges. As for sustainable development of students, the core and basic point is to develop their personality and teach them in accordance with their aptitude. As a bridge and link between teaching thinking and practice of teachers, their teaching decisions need to fully take into consideration the sustainable development of students, which is of great significance to future employment, job transfer and promotion of students. Therefore, this paper studies the teaching decision optimization (TDO) for the sustainable development of students. First of all, a related teaching decision model is constructed. Then this paper proposes the particle swarm optimization algorithm (PSO) of teaching decision path based on progress rate, gives the principle of selecting TDO template, designs three different optimization models for ordinary particles, high-quality particles and progress particles and represents the optimization strategy of teaching decision path. Finally, experimental results verify the effectiveness of the optimization model.

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“…However, existing classroom teaching decision-making methods often fail to meet students' personalized learning needs, resulting in some students being unable to reach their full potential in the classroom [14][15][16][17]. It is difficult for those traditional methods to fully consider the personalized needs of students, because their teaching design mainly relies on the teaching syllabus and the personal experience of teachers [18][19][20][21]. In this situation, the teaching contents and methods are often not diversified, which is not conducive to stimulating students' interests and initiative in learning.…”

Section: Introductionmentioning

confidence: 99%

Personalizing Students' Learning Needs by a Teaching Decision Optimization Method

Zhang,

Shi,

2023

Int. J. Emerg. Technol. Learn.

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With the rapid development of educational technology and the deepening of educational system reform, personalized education has gradually become an important topic in education. However, existing classroom teaching decision-making methods often fail to meet students’ personalized learning needs, resulting in some students being unable to reach their full potential in the classroom. To solve this problem, this study proposed a multi-conditional factor classroom teaching decision optimization method based on the improved particle swarm optimization (IPSO) algorithm, and predicted students’ personalized learning needs by combining with the improved ant colony optimization-support vector regression (IACO-SVR) model. First, the IACO-SVR model was used to collect students’ learning data, such as grades, interests, hobbies and learning progress, to accurately predict their needs in different teaching contexts. Second, the IPSO algorithm was used to optimize the multi-conditional factor classroom teaching decisions, thus meeting the personalized needs of students. The IPSO algorithm had strong global search ability, which effectively found the optimal solution to achieve personalized teaching strategies. It is expected that the teaching quality can be improved by predicting the personalized learning needs of students and optimizing classroom teaching decisions in this study, thus providing better support for their comprehensive development. In addition, the results of this study can provide theoretical basis and reference for administrative departments of education and schools to formulate personalized education policies.

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A Decision-Making Model for Teaching Higher Order Thinking Skills (HOTS) to College Students

Cited by 3 publications

References 3 publications

The Efficacy of the MOOC+SPOC Synchronous Learning Model in Vocational Education for Process Control Systems

The Efficacy of the MOOC+SPOC Synchronous Learning Model in Vocational Education for Process Control Systems

Teaching Decision Optimization for Sustainable Development of Students

Personalizing Students' Learning Needs by a Teaching Decision Optimization Method

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