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The purpose of this paper is to propose a novel improved teaching and learning-based algorithm (TLBO) to enhance its convergence ability and solution accuracy, making it more suitable for solving large-scale optimization issues.

Utilizing multiple cooperation mechanisms in teaching and learning processes, an improved TBLO named CTLBO (collectivism teaching-learning-based optimization) is developed. This algorithm introduces a new preparation phase before the teaching and learning phases and applies multiple teacher–learner cooperation strategies in teaching and learning processes. Applying modularization idea, based on the configuration structure of operators of CTLBO, six variants of CTLBO are constructed. For identifying the best configuration, 30 general benchmark functions are tested. Then, three experiments using CEC2020 (2020 IEEE Conference on Evolutionary Computation)-constrained optimization problems are conducted to compare CTLBO with other algorithms. At last, a large-scale industrial engineering problem is taken as the application case.

Experiment with 30 general unconstrained benchmark functions indicates that CTLBO-c is the best configuration of all variants of CTLBO. Three experiments using CEC2020-constrained optimization problems show that CTLBO is one powerful algorithm for solving large-scale constrained optimization problems. The application case of industrial engineering problem shows that CTLBO and its variant CTLBO-c can effectively solve the large-scale real problem, while the accuracies of TLBO and other meta-heuristic algorithm are far lower than CLTBO and CTLBO-c, revealing that CTLBO and its variants can far outperform other algorithms. CTLBO is an excellent algorithm for solving large-scale complex optimization issues.

The innovation of this paper lies in the improvement strategies in changing the original TLBO with two-phase teaching–learning mechanism to a new algorithm CTLBO with three-phase multiple cooperation teaching–learning mechanism, self-learning mechanism in teaching and group teaching mechanism. CTLBO has important application value in solving large-scale optimization problems.

Research shows that postsecondary students are largely unaware of the impact of algorithms on their everyday lives. Also, most noncomputer science students are not being taught about algorithms as part of the regular curriculum. This exploratory, qualitative study aims to explore subject-matter experts’ insights and perceptions of the knowledge components, coping behaviors and pedagogical considerations to aid faculty in teaching algorithmic literacy to postsecondary students.

Eleven semistructured interviews and one focus group were conducted with scholars and teachers of critical algorithm studies and related fields. A content analysis was manually performed on the transcripts using a mixture of deductive and inductive coding. Data analysis was aided by the coding software program Dedoose (2021) to determine frequency totals for occurrences of a code across all participants along with how many times specific participants mentioned a code. Then, findings were organized around the three themes of knowledge components, coping behaviors and pedagogy.

The findings suggested a set of 10 knowledge components that would contribute to students’ algorithmic literacy along with seven behaviors that students could use to help them better cope with algorithmic systems. A set of five teaching strategies also surfaced to help improve students’ algorithmic literacy.

This study contributes to improved pedagogy surrounding algorithmic literacy and validates existing multi-faceted conceptualizations and measurements of algorithmic literacy.

The purpose of this paper is to propose a method of optimal trajectory planning for robotic manipulators that applies an improved teaching-learning-based optimization (ITLBO) algorithm.

The ITLBO algorithm possesses better ability to escape from the local optimum by integrating the original TLBO with variable neighborhood search. The trajectory of robotic manipulators complying with the kinematical constraints is constructed by fifth-order B-spline curves. The objective function to be minimized is execution time of the trajectory.

Experimental results with a 6-DOF robotic manipulator applied to surface polishing of metallic workpiece verify the effectiveness of the method.

The presented ITLBO algorithm is more efficient than the original TLBO algorithm and its variants. It can be applied to any robotic manipulators to generate time-optimal trajectories.

The purpose of this paper is to identify the effects of algorithm teaching on the problem-solving skills of deaf-hard hearing students.

In this research, a pre-test and post-test problem-solving scale was applied to the single group (16 deaf-hard hearing students at a secondary school level) that had received algorithm education. Pre-test and post-test results were compared in order to see whether there was a significant difference among students in terms of their problem-solving attitudes. Students’ levels of performing the applications were examined through observation forms and their opinions about algorithm teaching were received.

As a result of the research, it was determined that implemented algorithm teaching had a significant effect on improving the problem-solving skills of the students.

Scratch training can be administered as either a compulsory or an optional course for hearing students as the Scratch programme offers the opportunity of teaching algorithmic reasoning with games, making the courses entertaining and giving students the chance to create their own designs which helps to improve their creative problem-solving skills and their motivation accordingly. Scratch teaching can be beneficial in developing students’ problem-solving behaviours and creativity.

This paper aims to deal with the development of a newly improved version of teaching learning based optimization (TLBO) algorithm.

Random local search part was added to the classic optimization process with TLBO. The new version is called TLBO algorithm with random local search (TLBO-RLS).

At first step and to validate the effectiveness of the new proposed version of the TLBO algorithm, it was applied to a set of two standard benchmark problems. After, it was used jointly with two-dimensional non-linear finite element method to solve the TEAM workshop problem 25, where the results were compared with those resulting from classical TLBO, bat algorithm, hybrid TLBO, Nelder–Mead simplex method and other referenced work.

New TLBO-RLS proposed algorithm contains a part of random local search, which allows good exploitation of the solution space. Therefore, TLBO-RLS provides better solution quality than classic TLBO.

Teacher education and professional development have long been criticized for failing to induct and engage teachers in discourses of classroom deliberation. Lesson studies have contributed to an emerging discourse in which teachers come together to study classroom teaching to improve student learning. The purpose of this paper is to share knowledge about developing video resources to support this emerging discourse.

The authors showcase a digital hypermedia video case developed from research lessons on a third‐grade topic on division with remainder, conducted by teachers and researchers in a lesson study cycle in Singapore. Drawing on anchored instruction and knowledge points of the mathematics education discourse in China, the authors used embedded contexts, case‐based reasoning, critical incidents and facilitation as major design features.

A video documentary traces the research problem and how teachers learned to use the concrete‐pictorial‐abstract (CPA) model to improve teaching for student learning. Critical incidences are created to engage teachers in analyzing the research lessons by describing, interpreting and probing into the object of learning, student difficulties in learning, and how the teacher mediated the subject matter of teaching. A full range of lesson study data and related reading and web resources are provided in the video case to support training and self study.

The paper demonstrates the promise to capitalize on the curricular and pedagogical values of the rich video archives of lesson studies to support continued inquiry of teachers. It has important implications for addressing the issues of depth of implementation and sustainability arising from rapid spread of lesson studies in countries like Singapore.

The purpose of this paper is to compare the effect of different representations while teaching basic algorithmic concepts to novice programmers.

A learning activity was designed and mediated with two conceptually different learning environments, each one used by a different group. The first group used the learning environment “Visual Flowchart”, which enables the students to construct and examine an algorithm using visual representation based on actual flowchart objects. The second group used the software “Language Interpreter”, which allows the students to express an algorithms using pseudocode.

Analysis of results among the two groups showed no statistically significant differences in the students’ performance with respect to the tool they used to solve the activity, the school stream they followed in high school and their gender.

The lack of difference among the two groups could be attributed to the non‐complicated nature of the given activity. In addition, longitudinal studies of the effect of the different representation in the frame of an introductory first semester academic course in computer science could further validate the results.

Two alternative learning environments aimed to support learning of basic programming skills.

Two alternative learning environments were presented and discussed in detail, aimed to support learning of basic programming skills. The conclusions of the present study are in contrast to the research that has taken place in the past which compared usage of flowcharts and pseudocode to educate novice programmers, and wider adoption of “flowcharts” was depicted.

The purpose of this paper is to solve the realistic problem of source maintenance scheduling (SMS) based on reliability criterion. A novel effective optimization technique is proposed to solve the problem at hand.

The problem has been formulated as a combinatorial optimization task, with the goal of maximizing reliability by minimizing the sum of squares of the reserve loads while satisfying unit and system constraints. This paper employs a nature inspired algorithm known as Teaching Learning Based Optimization (TLBO) for solving the SMS problem based on reliability.

The results reveal that optimal maintenance schedules of generating units has been obtained using TLBO algorithm with minimized values of sum of squares of reserve loads while satisfying system and operational constraints. It is also found that the inclusion of resource constraints (RC) in the model have significant effects on the objective function value which provides a deep insight of the proposed methodology.

The contribution of this paper is that an efficient nature inspired algorithm has been applied to solve source maintenance scheduling problem in viewpoint of the planning for future system capacity expansion. The incorporation of exclusion and RC in the model makes the analysis about the impact of SMS on the system reliability more reasonable.

The purpose of this paper is to present the development of a system called Structured Hypermedia Algorithm Explanation (SHALEX), as a remedy for the limitations existing within the current traditional algorithm animation (AA) systems. SHALEX provides several novel features, such as use of invariants, reflection of the high‐level structure of an algorithm rather than low‐level steps, and support for programming the algorithm in any procedural or object‐oriented programming language.

By defining the structure of an algorithm as a directed graph of abstractions, algorithms may be studied top‐down, bottom‐up, or using a mix of the two. In addition, SHALEX includes a learner model to provide spatial links, and to support evaluations and adaptations.

Evaluations of traditional AA systems designed to teach algorithms in higher education or in professional training show that such systems have not achieved many expectations of their developers. One reason for this failure is the lack of stimulating learning environments which support the learning process by providing features such as multiple levels of abstraction, support for hypermedia, and learner‐adapted visualizations. SHALEX supports these environments, and in addition provides persistent storage that can be used to analyze students' performance. In particular, this storage can be used to represent a student model that supports adaptive system behavior.

SHALEX is being implemented and tested by the authors and a group of students. The tests performed so far have shown that SHALEX is a very useful tool. In the future additional quantitative evaluation is planned to compare SHALEX with other AA systems and/or the concept keyboard approach.

SHALEX has been implemented as a web‐based application using the client‐server architecture. Therefore students can use SHALEX to learn algorithms both through distance education and in the classroom setting.

This paper presents a novel algorithm explanation system for users who wish to learn algorithms.

The purpose of this paper is to propose an indexing and teaching focus mining system for lecture videos recorded in an unconstrained environment.

By applying the proposed algorithms in this paper, the slide structure can be reconstructed by extracting slide images from the video. Instead of applying traditional shot‐change detection methods for general videos, a new edge‐based shot‐change detection algorithm is designed specifically for lecture videos. Besides, light influence and occlusions in the lecture video can be removed to obtain more accurate results. Moreover, the teaching focus can be extracted according to instructors' behavior based on the analyses of visual and audio information extracted from the lecture video.

Experiment results show the feasibility of the proposed method, that is, the slide shots can be correctly detected even if the illumination conditions are variant or the slides are obstructed by the instructor or students, and the teaching focus can be extracted to provide learners with an efficient way to study.

This paper provides only technical experiments, but lacks complete educational study. In the future, more subjective tests will be designed to examine the educational effects on students.

This paper proposes a practical indexing and teaching focus mining system for lecture videos which can help students learn.

The proposed algorithms for indexing and teaching focus mining are derived and applied in lecture videos in this paper.