Practical Scheduling Research Articles

An adaptive decision support system for outpatient appointment scheduling with heterogeneous service times

Appointment scheduling (AS) plays a crucial role in outpatient clinic management. Traditional methods involve patient grouping using pre-defined rules and scheduling based on these groups. However, pre-defined rules may not adequately capture the heterogeneity in patients’ service times (i.e., consultation duration). Advanced machine learning (ML) methods can address individual-level heterogeneity but pose challenges for practical scheduling. To strike a balance, we propose a data-driven AS decision support system, Cluster-Predict-Schedule (CPS), integrating both supervised and unsupervised ML for efficient patient grouping and scheduling. The novelty of CPS lies in its adaptability to service time heterogeneity through a data-driven approach, determining patient groups based on data rather than pre-defined rules. Additionally, CPS includes a generic and efficient algorithm for generating appointment templates adaptable to any number of patient groups. Our system’s efficacy is demonstrated using a real-world dataset. Evaluated by the weighted sum of patient wait times, physician idle time, and overtime, CPS achieves up to 15.0% cost reduction compared to the FCFA (first-call, first-appointment) scheme and over 4.7% savings against the common New/Return classification with traditional sequencing candidate (TSC) rules. In addition, CPS enhances outpatient operational efficiency without compromising fairness.

Enhancing perceptual, attentional, and working memory demands through variable practice schedules: insights from high-density EEG multi-scale analyses.

Contextual interference (CI) enhances learning by practicing motor tasks in a random order rather than a blocked order. One hypothesis suggests that the benefits arise from enhanced early perceptual/attentional processes, while another posits that better learning is due to highly activated mnemonic processes. We used high-density electroencephalography in a multi-scale analysis approach, including topographic analyses, source estimations, and functional connectivity, to examine the intertwined dynamics of attentional and mnemonic processes within short time windows. We recorded scalp activity from 35 participants as they performed an aiming task at three different distances, under both random and blocked conditions using a crossover design. Our results showed that topographies associated with processes related to perception/attention (N1, P3a) and working memory (P3b) were more pronounced in the random condition. Source estimation analyses supported these findings, revealing greater involvement of the perceptual ventral pathway, anterior cingulate and parietal cortices, along with increased functional connectivity in ventral alpha and frontoparietal theta band networks during random practice. Our results suggest that CI is driven, in the random compared to the blocked condition, by enhanced specific processes such as perceptual, attentional, and working memory processes, as well as large-scale functional networks sustaining more general attentional and executive processes.

Application of the maturity model for collaborative scheduling for construction projects

PurposeThis paper introduces the weighting, analysis and validation method used in the development of the Maturity Model for Collaborative Scheduling (MMCS). The scoring and ranking process introduced by the MMCS fills a gap in the literature by supporting the selection of collaborative scheduling (CS) practices that yield more weight toward the achievement of higher maturity levels in the development and implementation of CS. The ranking process can then be used during pre/post project execution to track collaborative scheduling in practice against the model’s weighting and provide the project team with constructive feedback and actionable steps for reaching the next highest level of collaboration.Design/methodology/approachThe MMCS, which focuses on five pillars (key areas of interest for CS) and related swim lanes (specific attributes), covers a broad range of areas in the construction industry and was coded into a survey. The relative weights of pillars and swim lanes were then established using the Delphi method with the group of subject matter experts (SMEs), analyzed using multi-objective decision analysis (MODA) and validated using 241 answers to a survey with questions drawn from the MMCS, including organizations across the industry in the United States.FindingsThe project scoring defines bounds for bronze, silver and gold levels of collaboration in scheduling. Project evaluations can then be used to identify areas for continuous improvement and enhanced collaboration. We offer recommendations and best practices for project improvement.Originality/valueTwo original contributions resulted from this work: (1) a method to elicit weights based on a combination of Delphi, MODA and survey methods was used to develop and validate a scale with three different maturity levels to support the use and continuous improvement of CS practices and (2) a validated model was used to assess the maturity level of CS in construction projects alongside specific recommendations to move upward in terms of maturity. In practice, project leaders can use this model to assess project performance, advance the project’s maturity and guide continuous improvement efforts for enhanced collaboration.

Promoting health literacy of older post-discharge patients in general practice - Creation of the GP-MATE communication tool through co-design

ObjectiveWork with older patients and their carers to co-design a tool that improves patient - general practice communication and continuity of care following discharge of an older person from hospital. MethodsExperience Based Co-Design with three teams of six to seven lay people (older patients and their carers), each supported by a corresponding general practice group. The process included an implementation-focused event with participants using the intervention in a live role-play. ResultsCo-design generated a patient-held tool (GP-MATE) that focuses on four areas of post-discharge care: carers/caring; continuity; medication safety and information power. Access to general practice for patients/carers post-discharge was considered to be vital to improving communication. Discussion and conclusionThe co-design process enabled patients and carers to be involved through all stages of intervention development, ensuring relevance and alignment. Practice implicationsThe intervention is uniquely suited to general practice, comprehensive yet brief enough to be usable within a 20-minute consultation. While the domains of GP-MATE compare well with existing care transitions literature, it will be important to assess impact on already busy practice schedules and impact on care.

Real-time scheduling for two-stage assembly flowshop with dynamic job arrivals by deep reinforcement learning

Assembly flowshops differ from flowshops because their jobs have several components that are manufactured in the processing stage and then joined together in the assembly stage. Real-time scheduling of two-stage assembly flowshops is of great significance for many discrete manufacturing companies in customized order fulfilment. Therefore, we study two-stage assembly flowshop scheduling problem with the objective of total tardiness minimization considering dynamic job arrivals using deep reinforcement learning (DRL). First, an architecture of DRL-based real-time scheduling is proposed. Second, we establish a DRL-based real-time scheduling model by designing proper state space, action space and reward function. A scheduling agent is constructed and trained through the proximal policy optimization (PPO) using interactive simulation data. The experimental results show that the trained scheduling agent can learn to choose the appropriate dispatching rules efficiently so as to make a real-time scheduling quickly and effectively. The PPO algorithm outperforms six dispatching rules, genetic algorithm and two DRL algorithms (i.e., DDQN and DDPG) in terms of solution quality and computing speed. The proposed approach is highly applicable to many practical scheduling situations that requires real-time and quick decisions.

Short-Term Hydro-Wind-PV peak shaving scheduling using approximate hydropower output characters

With the massive construction of wind and photovoltaic (PV) power plants, the uncertainty of their output poses challenges for grid peak regulation. Hydropower, characterized by convenient regulation, fast response speed, and low cost, is an ideal choice for compensating for wind and PV energy generation. Mixed-Integer Linear Programming (MILP) is employed to work out short-term scheduling of hydro-wind-PV power. However, considering hydropower scheduling alone is already highly complex, and incorporating the deviations in wind and PV energy further reduces the solution efficiency. In practical scheduling, operators often rely on empirical scheduling methods that can yield satisfactory results. To enhance solution quality and improve the scalability and efficiency of the MILP method, a short-term optimization scheduling model for hydropower plants has been proposed and applied to the Hongshui River cascade system. The case study shows that the model can perform calculations in just 2 s, demonstrating extremely high computational efficiency. This can significantly enhance decision-making in cascaded hydropower scheduling. At the same time, the model leaves sufficient reserve capacity to address the uncertainty of wind and solar power output while reducing the residual load by 0.8 %, which is beneficial for the stable operation of the grid.

Two-agent proportionate flowshop scheduling with deadlines: polynomial-time optimization algorithms

Volatility in the supply chain of critical products, notably the vaccine shortage during the pandemic, influences livelihoods and may lead to significant delays and long waiting times. Considering the capital- and time-intensive nature of capacity expansion plans, strategic operational production decisions are required best to address the supply-demand mismatches given the limited production resources. This research article investigates the production scenarios where the demand of one agent must be completed within a specified due date, hereinafter referred to as the deadline, while minimizing the maximum or total completion time of another agent's demand. For this purpose, the Two-Agent Proportionate Flowshop Scheduling Problem with deadlines is introduced. Two polynomial-time optimization algorithms are developed to solve these optimization problems. This study will serve as a basis for further developing this practical yet understudied scheduling problem.

Sustainable scheduling of TFT-LCD cell production: A hybrid dispatching rule and two-phase genetic algorithm

TFT-LCD manufacturing process involves Array, Color Filter (CF), Cell, and Module. The Cell process is pivotal in the TFT-LCD production supply chain; it bridges the front-end (Array and CF) and back-end (Module), maintaining a balance of production capacities. Supply from the front-end is transmitted to the back-end, ensuring prompt responsiveness to customer demands. As awareness of environmental conservation grows, implementing sustainable development in the TFT-LCD manufacturing industry has become crucial. Specifically, reducing the machine setup frequency in TFT-LCD scheduling decreases energy consumption during machine operation and wait times and prolongs equipment lifespan by minimizing wear from repeated shutdowns. To tackle the intricate scheduling challenges within TFT-LCD production, this study proposes an intelligent, sustainable scheduling approach that employs a rolling dispatching and line balancing mechanism and a two-phase genetic algorithm (HDTPGA) for Cell scheduling. The goal is to minimize makespan considering product diversity, production constraints, work-in-process balancing, and machine setup frequency. This holistic approach promotes energy-efficient and sustainable manufacturing practices. According to the empirical study, the proposed HDTPGA reduces the makespan by approximately 10% compared to the baseline. Additionally, it reduces the average daily setup frequency of bottleneck stages (ODF and CCT) in the Cell process by approximately 68.25% and 66.25%, respectively. The main contribution of this paper is to propose an intelligent scheduling algorithm to optimize Cell production scheduling in practice. The HDTPGA achieves equilibrium between front-end and back-end production, utilizing an efficient line change mechanism to reduce energy consumption. The enhancement in efficiency not only restricts material waste but also reduces the environmental impact, enabling manufacturers to adopt green production practices and promoting a transition of the entire panel industry toward sustainability.

Durability of retrieval-induced forgetting: Effects of different practice schedules.

If retrieval-induced forgetting (RIF) is to play a role in the formation of collective memories, it should be long lasting. Although several studies have found that RIF is short-lived, there is other evidence to suggest that repeated selective practice schedules with a temporal gap between each practice trial may increase the durability of RIF. We tested this possibility in three experiments, focusing on socially shared retrieval-induced forgetting (SSRIF). In two experiments, participants studied scientific or story materials, then listened to someone selectively recall the material repeatedly, either in rapid succession or over an extended time period, and finally recalled the original materials either immediately, after a 1-week delay, or after a 3-week delay. A third experiment examined selective practice in free-flowing conversations. In each instance, RIF was found with repeated selective practice with a temporal gap between trials. The results are discussed in terms of the role RIF might play in the formation of collective memory.

Developing Evidence-Based Practices: Lessons Learned from Implementing a Collaborative, Cross-Agency Substance Use Family Intervention

ABSTRACT Me and my family is a knowledge-based motivational program that includes eight weekly therapeutic sessions provided via the social services for families in which parent(s) have substance use problems (SUP). Purpose The aim of this study was to report experiences gained while conducting the research project, in which social workers’ active participation was a core feature. Materials and method Guided by the implementation framework AIF (Active Implementation Framework) data were collected from multiple sources, such as minutes from guidance sessions, meetings with managers, booster days, and transcriptions of focus group interviews with social workers. Results Findings indicate that social workers need to receive training in research activities before program start and to have time for pre- and post-therapeutic session reflections To deliver and maintain an intervention like Me & my Family require cross-sectional cooperation and staff having academic degrees to develop reflections on program delivery. Research design experiences indicate that RCT methods proved difficult to accomplish. An additional finding was organizational structures in silos as a barrier to recruiting families. Conclusion Involving social workers in doing research on practices concern needs for managerial support regarding scheduling, routines and interest in evidence-based practice, well-educated social workers, and special attention to research design in relation to clients’ needs. Conclusions drawn from using the AIF framework include the need for continuous coaching and consultations as well as feed-back on results.

A Flow Shop Scheduling Method Based on Dual BP Neural Networks with Multi-Layer Topology Feature Parameters

Nowadays, the focus of flow shops is the adoption of customized demand in the context of service-oriented manufacturing. Since production tasks are often characterized by multi-variety, low volume, and a short lead time, it becomes an indispensable factor to include supporting logistics in practical scheduling decisions to reflect the frequent transport of jobs between resources. Motivated by the above background, a hybrid method based on dual back propagation (BP) neural networks is proposed to meet the real-time scheduling requirements with the aim of integrating production and transport activities. First, according to different resource attributes, the hierarchical structure of a flow shop is divided into three layers, respectively: the operation task layer, the job logistics layer, and the production resource layer. Based on the process logic relationships between intra-layer and inter-layer elements, an operation task–logistics–resource supernetwork model is established. Secondly, a dual BP neural network scheduling algorithm is designed for determining an operations sequence involving the transport time. The neural network 1 is used for the initial classification of operation tasks’ priority; and the neural network 2 is used for the sorting of conflicting tasks in the same priority, which can effectively reduce the amount of computational time and dramatically accelerate the solution speed. Finally, the effectiveness of the proposed method is verified by comparing the completion time and computational time for different examples. The numerical simulation results show that with the increase in problem scale, the solution ability of the traditional method gradually deteriorates, while the dual BP neural network has a stable performance and fast computational time.

Analysis of the Governance System of National Health Insurance Referral Patients at the Outpatient Polyclinic of Dian Harapanjayapura Hospital, Papua

The National Health Insurance (JKN) program, which has been running since January 1, 2014, has been widely benefited by most of the community. This program makes it easy for the public to access health services. However, the implementation of JKN for nine years often faces problems and challenges, one of which is governance. Many parties involved in this JKN program include BPJS Kesehatan as the organizer, the Ministry of Health as a regulator, Health Facilities (hospitals) as service providers and Professional Associations. This research is a qualitative research with a type of program review (governance system). The data collection process is carried out by conducting observations, interviews, analysis and documentation. Data analysis is in the form of an explanation of the situation being studied, while the presentation is in the form of a story explanation. This is in accordance with descriptive research which is the study of observations on the object of research to obtain an overview and actual circumstances. This research has been conducted at Dian Harapan Jayapura Hospital in Papua in February 2024. Based on the results of the study, it was found that inputs, processes and outputs related to the governance of National Health Insurance referral patients at the Outpatient Polyclinic of Dian Harapan Jayapura Hospital Papua were based on an assessment of human resources, facilities and infrastructure, Standard Operational Procedures, constraints and problem solving processes. The results of the study based on the influence of Human Resources show that training related to BPJS has not been carried out evenly for all personnel, including the evaluation of services has not been carried out regularly. The Assessment of Facilities and Infrastructure is considered to have been fulfilled only due to disturbances in the queue machine, the V-Claim application and Scan Finger interference. The assessment of SPO is considered to be available and understood, but there has been no routine evaluation to assess the progress of SPO implementation. The assessment of the obstacles that are often experienced is the disruption of the V-Claim Application and the queue system is still offline because the patient has not used the Mobile JKN Application so that the patient comes early in the morning to take the queue number. In terms of handling problems in stages starting from internal, if necessary, coordination between units or coordination with management with higher authority will be carried out. The conclusion in this study is that Human Resources have a great influence on services, in terms of the number that has been fulfilled, it is still necessary to evaluate the progress of services such as obstacles to delays in the doctor's practice schedule to be corrected according to the agreement so as not to affect service satisfaction. Regarding Facilities and Infrastructure, it is still considered good, it only needs to be added to facilitate online registration by increasing the benefits of the Mobile JKN Application, including the Queue Display facility at the Polyclinic. The applicable Standard Operational Procedures have been implemented, but there has not been a routine evaluation of the implementation of the SPO has been carried out appropriately or still requires additional SPOs to support services. Regarding obstacles and the solution of obstacles, it is considered good. Each officer is already independent in handling problems.

An Ethnographic Exploration of Performance-Based Learning in Chinese University Music Education

This ethnographic study explores the impact of performance-based learning on music education at Langfang Teachers University in China. Through observations and interviews, the study examines how live demonstrations, student performances, and ensemble practices contribute to students' technical proficiency, musical interpretation, collaborative skills, and self-confidence. The findings reveal high levels of student engagement and appreciation for hands-on learning, with significant improvements in instrumental and vocal skills. However, challenges such as performance anxiety, resource limitations, time management issues, and varying levels of preparedness were also identified. Recommendations include implementing workshops for anxiety management, increasing funding for facilities, developing structured practice schedules, and introducing peer mentoring programs. The study underscores the importance of integrating traditional Chinese music with contemporary practices, ensuring a well-rounded and culturally enriched music education that prepares students for diverse careers in the global music industry.

Motor interference on lateral pelvis shifting towards the paretic leg during walking and its cortical mechanisms in persons with stroke.

Motor interference, where new skill acquisition disrupts the performance of a previously learned skill, is a critical yet underexplored factor in gait rehabilitation post-stroke. This study investigates the interference effects of two different practice schedules, applying interleaved (ABA condition) and intermittent (A-A condition) pulling force to the pelvis during treadmill walking, on lateral pelvis shifting towards the paretic leg in individuals with stroke. Task A involved applying resistive pelvis force (pulling towards the non-paretic side), and Task B applied assistive force (pulling towards the paretic side) at the stance phase of the paretic leg during walking. Sixteen individuals with chronic stroke were tested for gait pattern changes, including lateral pelvis shifting and spatiotemporal gait parameters, and neurophysiological changes, including muscle activity in the paretic leg and beta band absolute power in the lesioned cortical areas. A-A condition demonstrated increased lateral pelvis shifting towards the paretic side, extended paretic stance time and longer non-paretic step length after force release while ABA condition did not show any changes. These changes in gait pattern after A-A condition were accompanied by increased muscle activities of the ankle plantarflexors, and hip adductors/abductors. A-A condition demonstrated greater changes in beta band power in the sensorimotor regions compared to ABA condition. These findings suggest that while walking practice with external force to the pelvis can improve lateral pelvis shifting towards the paretic leg post-stroke, practicing a new pelvis shifting task in close succession may hinder the performance of a previously obtained lateral pelvis shifting pattern during walking.

The effect of contextual interference on transfer in motor learning - a systematic review and meta-analysis.

Since the initial study on contextual interference (CI) in 1966, research has explored how practice schedules impact retention and transfer. Apart from support from scientists and practitioners, the CI effect has also faced skepticism. Therefore, we aimed to review the existing literature on the CI effect and determine how it affects transfer in laboratory and applied settings and in different age groups. We found 1,287 articles in the following databases: Scopus, EBSCO, Web of Science, ScienceDirect, supplemented by the Google Scholar search engine and manual search. Of 300 fully screened articles, 42 studies were included in the systematic review and 34 in the quantitative analysis (meta-analysis). The overall CI effect on transfer in motor learning was medium (SMD = 0.55), favoring random practice. Random practice was favored in the laboratory and applied settings. However, in laboratory studies, the medium effect size was statistically significant (SMD = 0.75), whereas, in applied studies, the effect size was small and statistically non-significant (SMD = 0.34). Age group analysis turned out to be significant only in adults and older adults. In both, the random practice was favored. In adults, the effect was medium (SMD = 0.54), whereas in older adults was large (SMD = 1.28). In young participants, the effect size was negligible (SMD = 0.12). Systematic review registration: https://clinicaltrials.gov/, identifier CRD42021228267.

Peripheral nerve injuries in the performing artist.

Dancers and musicians have unique physical demands that can lead to injury of the peripheral nerves. Specific dance movements and specific instrument positions, combined with countless hours of practice and repetition, create an environment for potential nerve injury. Familiarity with these variables and recognition of the common presentations of neuropathic syndromes are essential in the evaluation of a performing artist with a suspected peripheral nerve injury. Assessment should include an understanding and analysis of their dance style or instrument playing posture, particularly in the position or motion that recreates the symptoms if possible. Practice and performance schedules should also be considered. Diagnosis may require electrodiagnostic testing, imaging, or diagnostic injections. Treatment should be comprehensive and may include modifications in practice schedule, posture/position, and technique in addition to consideration of medications, splints/orthoses, physical therapy, and injections. The instructor/teacher should be involved in the treatment plan if applicable. Complete rest in this population may not be realistic or necessary. Early and accurate diagnosis of nerve injury is important for safe return to dance or instrumental music.

Automated construction scheduling using deep reinforcement learning with valid action sampling

The growing demand for buildings and infrastructures requires optimal construction schedules under real-world complexities. This paper presents an automated scheduling optimization method for large construction projects, considering real-world constraints and the need for rescheduling. A Deep Reinforcement Learning (DRL) model with a Valid Action Sampling (VAS) mechanism is proposed to optimize schedules. The method integrates a Graph Convolutional Network (GCN) for feature extraction and includes a reward shaping mechanism to expedite convergence. The proposed method outperforms traditional methods with reduced project duration and runtime in both scheduling and rescheduling cases. This advancement benefits construction managers seeking efficient and flexible project management. The findings inspire future research into broader and more practical construction scheduling solutions utilizing DRL.

Algorithmic Spaced Retrieval Enhances Long-Term Memory in Alzheimer Disease: Case-Control Pilot Study.

Spaced retrieval is a learning technique that involves engaging in repeated memory testing after increasingly lengthy intervals of time. Spaced retrieval has been shown to improve long-term memory in Alzheimer disease (AD), but it has historically been difficult to implement in the everyday lives of individuals with AD. This research aims to determine, in people with mild cognitive impairment (MCI) due to AD, the efficacy and feasibility of a mobile app that combines spaced retrieval with a machine learning algorithm to enhance memory retention. Specifically, the app prompts users to answer questions during brief daily sessions, and a machine learning algorithm tracks each user's rate of forgetting to determine the optimal spacing schedule to prevent anticipated forgetting. In this pilot study, 61 participants (young adults: n=21, 34%; healthy older adults: n=20, 33%; people with MCI due to AD: n=20, 33%) used the app for 4 weeks to learn new facts and relearn forgotten name-face associations. Participation during the 4-week period was characterized by using the app once per day to answer 15 questions about the facts and names. After the 4-week learning phase, participants completed 2 recognition memory tests approximately 1 week apart, which tested memory for information they had studied using the app as well as information they had not studied. After using the mobile app for 1 month, every person with MCI due to AD demonstrated improvements in memory for new facts that they had studied via the app compared to baseline (P<.001). All but one person with MCI due to AD (19/20, 95%) showed improvements of more than 10 percentage points, comparable to the improvements shown by young adults and healthy older adults. Memory for name-face associations was similarly improved for all participant groups after using the app but to a lesser degree. Furthermore, for both new facts and name-face associations, we found no memory decay for any participant group after they took a break of approximately 1 week from using the app at the end of the study. Regarding usability, of the 20 people with MCI due to AD, 16 (80%) self-adhered to the app's automated practice schedule, and half of them (n=10, 50%) expressed an interest in continuing to use it. These results demonstrate early evidence that spaced retrieval mobile apps are both feasible for people with early-stage AD to use in their everyday lives and effective for supporting memory retention of recently learned facts and name-face associations.

Practicing traditional cultural games skills according to random and game-based practice schedules can improve gross motor skills performance.

The aim of this research was to examine the effects of using a random and game-based practice schedule for Traditional Cultural Games (TCG) skills on the performance of gross motor skills. Specifically, skills of two types of TCG, Chindro, an individual TCG, and Pittu-Garam, a team TCG, were practiced. 102 primary school children, ages seven to ten, attended a total of eighteen sessions of skill practice in 6 weeks, practicing TCG motor skills found in the game of Chindro or Pittu-Garam. For each TCG, participants were assigned to either random or game-based conditions, contributing to four experimental groups (Chindro random, Chindro game-based, Pittu random, and Pittu game-based). Gross motor skills performance was measured, before and after the experiment, according to the Test of Gross Motor Development-2. Particularly, the performance of six gross motor skills, Catch, Overhead Throw, Underhand Roll, Hop, Leap, and Jump, were assessed. Additionally, through a transfer test, the same skills were evaluated from a real-world game situation. In the skills performance test, all four groups significantly improved gross motor skills performance by the end of the experiment. However, there was no difference found between the groups based on game-based and random selection in the skills performance test and the game transfer test for both TCG. Practicing TCG skills according to high interference practice schedules, whether individually or in combination, improved gross motor skills performance. The study indicated that a game-based practice schedule could be a substitute for a random practice schedule when planning a training session involving high-interference practice schedules.

High contextual interference improves retention in motor learning: systematic review and meta-analysis

The effect of practice schedule on retention and transfer has been studied since the first publication on contextual interference (CI) in 1966. However, strongly advocated by scientists and practitioners, the CI effect also aroused some doubts. Therefore, our objective was to review the existing literature on CI and to determine how it affects retention in motor learning. We found 1255 articles in the following databases: Scopus, EBSCO, Web of Science, PsycINFO, ScienceDirect, supplemented by the Google Scholar search engine. We screened full texts of 294 studies, of which 54 were included in the meta-analysis. In the meta-analyses, two different models were applied, i.e., a three-level mixed model and random-effects model with averaged effect sizes from single studies. According to both analyses, high CI has a medium beneficial effect on the whole population. These effects were statistically significant. We found that the random practice schedule in laboratory settings effectively improved motor skills retention. On the contrary, in the applied setting, the beneficial effect of random practice on the retention was almost negligible. The random schedule was more beneficial for retention in older adults (large effect size) and in adults (medium effect size). In young participants, the pooled effect size was negligible and statically insignificant.