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Learning Curve

The Learning Curve refers to the gradual improvement in performance or skill acquisition that occurs as an individual or group engages in a particular task or activity over time.
It describes the non-linear relationship between the amount of experience or practice and the level of proficiency achieved.
The learning curve can be influenced by factors such as task complexity, learner motivation, and the use of effective teaching or training methods.
Understanding the learning curve is crucial in fields like education, skills development, and human-computer interaction, as it helps optimize learning strategies and predict the time and effort required to master new skills.
Reserach on learning curves can provide insights into the cognitive processes underlying skill acquisition and help design more effective learning environments.

Most cited protocols related to «Learning Curve»

1
AutoDockTools: A Versatile Molecular Docking GUI
With the release of AutoDock3, it became apparent that the tasks of coordinate preparation, experiment design, and analysis required an effective graphical user interface to make AutoDock a widely accessible tool. AutoDockTools was created to fill this need. AutoDockTools facilitates formatting input molecule files, with a set of methods that guide the user through protonation, calculating charges, and specifying rotatable bonds in the ligand and the protein (described below). To simplify the design and preparation of docking experiments, it allows the user to identify the active site and determine visually the volume of space searched in the docking simulation. Other methods assist the user in specifying search parameters and launching docking calculations. Finally, AutoDockTools includes a variety of novel methods for clustering, displaying, and analyzing the results of docking experiments.
AutoDockTools is implemented in the object-oriented programming language Python and is build from reusable software components15 ,16 . The easy-to-use graphical user interface has a gentle learning curve and an effective self-taught tutorial is available online. Reusable software components are used to represent the flexible ligand, the sets of parameters and the docking calculation, enabling a range of uses from a single use to thousands of docking experiments involving many different sets of molecules, facilitating automated high-throughput applications. For example, converting the NCI diversity database of small molecules into AutoDock-formatted ligand files was possible with a short Python script of less than 20 lines by leveraging the existing software components underlying AutoDockTools.
AutoDockTools exists in the context of a rich set of tools for molecular modeling, the Python Molecular Viewer (PMV)16 ,17 (link). PMV is a freely distributed Python-based molecular viewer. It is built with a component-based architecture with the following software components: ViewerFramework, a generic OpenGL-based 3-dimensional viewing component; and MolKit, a hierarchical data representation of molecules. AutoDockTools consists of a set of commands dynamically extending PMV with commands specific to the preparation, launching and analysis of AutoDock calculations. Hence, all PMV commands (such as reading/writing files, calculating and displaying secondary structure, adding or deleting hydrogens, calculating charges and molecular surfaces, and many others) are also naturally available in AutoDockTools. PMV also provides access to the Python-interpreter so that commands or scripts can be called interactively. PMV commands log themselves, producing a session file that can be rerun. In summary, AutoDockTools is an example of a specialization of the generic molecular viewer PMV for the specific application of AutoDock.
Morris G.M., Huey R., Lindstrom W., Sanner M.F., Belew R.K., Goodsell D.S, & Olson A.J. (2009). AutoDock4 and AutoDockTools4: Automated Docking with Selective Receptor Flexibility. Journal of computational chemistry, 30(16), 2785-2791.
Publication 2009
Generic Drugs Hydrogen Learning Curve Ligands Proteins Python
2
Imaging Spine Dynamics in Mice
Young (1 month old) and adult (> 4 months old) mice expressing YFP in a small subset of cortical neurons (YFP-H line29 (link)) were used in all the experiments. Young mice were trained on the single-seed reaching task for up to 16 days and displayed a stereotypical learning curve (Fig. 1b). Naive adult mice and mice that had been previously trained with the single-seed reaching task in adolescence were trained with either the same reaching task or a novel capellini handling task for up to 8 days (see Methods). Apical dendrites of layer V pyramidal neurons, 10–100 μm below the cortical surface, were repeatedly imaged in mice under ketamine–xylazine anaesthesia with two-photon laser scanning microscopy. Spine dynamics in the motor cortex and other regions were followed over various intervals. Imaged regions were initially guided by stereotaxic measurements. In 14 mice, intracortical microstimulation (see Methods) was performed at the end of repetitive imaging to determine the location of acquired images relative to the functional forelimb motor map (Supplementary Fig. 2). In total, 32,079 spines from 209 mice were tracked over 2–4 imaging sessions, with 121 mice imaged twice, 79 mice three times and 9 mice imaged four times. Spine formation and elimination rates in each mouse were determined by comparing images of the same dendrites acquired at two time points; all changes were expressed relative to the total number of spines seen in the initial images. The number of spines analysed and the percentage of spine elimination and formation under various experimental conditions are summarized in Supplementary Table 1. To quantify spine size, calibrated spine head diameters were measured over time30 (link) (Supplementary Notes). All data are presented as mean ± s.d., unless otherwise stated. P-values were calculated using the Student's t-test. A non-parametric Mann–Whitney U-test was used to confirm all conclusions.
Xu T., Yu X., Perlik A.J., Tobin W.F., Zweig J.A., Tennant K., Jones T, & Zuo Y. (2009). Rapid formation and selective stabilization of synapses for enduring motor memories. Nature, 462(7275), 915-919.
Publication 2009
Adult Anesthesia Cortex, Cerebral Dendrites Head Ketamine Laser Scanning Microscopy Learning Curve Mice, Laboratory Motor Cortex Neurons Pyramidal Cells Stereotypic Movement Disorder Upper Extremity Vertebral Column Vision Xylazine
3
Automated Knee Cartilage and Meniscus Segmentation
The neural network model chosen for this problem is based on the U-Net architecture, which has previously shown promising results in the tasks of segmentation, particularly for medical images (15 (link),22 –25 ), and has fewer trainable parameters than the other popular segmentation architecture, SegNet (26 ). The U-Net architecture can be viewed in Figure E1 (online). The network takes a full image section as input and then, through a series of trainable weights, creates the corresponding section segmentation mask (22 ).
Our U-Net model uses a weighted cross-entropy loss function between the true segmentation value and the output for our model. The weighted cross-entropy function was used to account for the class imbalance of the volume that cartilage and meniscus compartments make up compared with the entire MR imaging volume. Details on this equation can be viewed in Appendix E1 (online).
To build the U-Net models, data in subjects from both the T1ρ-weighted and the DESS sets were divided into training, validation, and time-point testing sets with a 70/20/10 split and were then broken down into their respective two-dimensional (2D) sections to be used as inputs for the two sequence models. The time-point testing set for both data sets consisted of only follow-up studies corresponding to baseline studies in the training and validation data sets. This time-point hold-out data set was used as validation for the precision of the automatic segmentation longitudinally. A full breakdown of the T1ρ-weighted and DESS training, validation, and time-point testing data according to diagnostic group (ACL, OA, control) can be viewed in Table 2. The full 3D segmentation map was then generated by stacking the predicted 2D sections for a subject and then taking the largest 3D-connected component for each compartment class.
All U-Net models were implemented in Native TensorFlow, version 1.0.1 (Google, Mountain View, Calif). Model selection was made by using the 1-standard-error rule on the validation data set (27 ) (B.N., with 3 years of experience). For full learning specifications and learning curves of the U-Net, see Table E1 and Figure E2 (both online).
Norman B., Pedoia V, & Majumdar S. (2018). Use of 2D U-Net Convolutional Neural Networks for Automated Cartilage and Meniscus Segmentation of Knee MR Imaging Data to Determine Relaxometry and Morphometry. Radiology, 288(1), 177-185.
Publication 2018
Cartilage Catabolism Desmosine Entropy Learning Curve Meniscus
4
Generative Model for Drug-like Molecules
In the first stage, we pretrained a generative model on a ChEMBL21 (36 (link)) data set of approximately 1.5 million drug-like compounds so that the model was capable of producing chemically feasible molecules (note that this step does not include any property optimization). This network had 1500 units in a GRU (32 ) layer and 512 units in a stack augmentation layer. The model was trained on a graphics processing unit (GPU) for 10,000 epochs. The learning curve is illustrated in fig. S5.
The generative model has two modes of processing sequences—training and generating. At each time step, in the training mode, the generative network takes a current prefix of the training object and predicts the probability distribution of the next character. Then, the next character is sampled from this predicted probability distribution and is compared to the ground truth. Afterward, on the basis of this comparison, the cross-entropy loss function is calculated, and parameters of the model are updated. At each time step, in generating mode, the generative network takes a prefix of already generated sequences and then, like in the training mode, predicts the probability distribution of the next character and samples it from this predicted distribution. In the generative model, we do not update the model parameters.
At the second stage, we combined both generative and predictive models into one RL system. In this system, the generative model plays the role of an agent, whose action space is represented by the SMILES notation alphabet, and state space is represented by all possible strings in this alphabet. The predictive model plays the role of a critic estimating the agent’s behavior by assigning a numerical reward to every generated molecule (that is, SMILES string). The reward is a function of the numerical property calculated by the predictive model. At this stage, the generative model is trained to maximize the expected reward. The entire pipeline is illustrated in Fig. 1.
We trained a Stack-RNN as a generative model. As mentioned above, for training, we used the ChEMBL database of drug-like compounds. ChEMBL includes approximately 1.5 million of SMILES strings; however, we only selected molecules with the lengths of SMILES string of fewer than 100 characters. The length of 100 was chosen because more than 97% of SMILES in the training data set had 100 characters or less (see fig. S6).
Popova M., Isayev O, & Tropsha A. (2018). Deep reinforcement learning for de novo drug design. Science Advances, 4(7), eaap7885.
Publication 2018
Biological Models Character Entropy EPOCH protocol Learning Curve Pharmaceutical Preparations
5
Designing Effective Experience Sampling Methods
The nature of ESM data is appealing but its complexity often challenges researchers and clinicians. The data collection parameters (the actual design) that define the nature of the data can be more important than statistical techniques. Aspects to consider are item selection, item order, the time frame (eg, number of days), the intensity (eg, number of beeps per day; number of questions within a beep), the need for additional information (eg, sleep quality assessed in a morning questionnaire), the signalling algorithm (eg, random, fixed, beep-free periods, anticipation), the addition of event recording (eg, of stressors or panic attacks), application type and data storage.
Items from cross-sectional questionnaires are often unsuitable for repeated assessment in daily life. In one-time questionnaires, a reliable assessment of a construct is achieved by redundancy (multiple items in a sum score). Repeated (up to 10 times a day) answering of similar items is frustrating. Often, metaphors are used, but these lack variation within the day.21 (link) ESM information, such as current mood, activity and company, is assessed with single items, which can be combined to improve reliability: different aspects at one moment or the same items over time. ESM data are ecologically valid but correlational in nature.22 The current activity can be a cause as well as an effect of momentary mood states. Furthermore, different mental states have different natural flows. Anxiety, for example, fluctuates and is more contextually reactive than depression. With an ESM sampling frequency of 10 times a day, highly variable states will not be adequately represented. In that case, the process is under-sampled. Other slow changing states are often over-sampled. The actual ESM protocol is usually a compromise.
Event monitoring is often added to ESM protocols. For example, participants are asked to complete a questionnaire when a certain stressor is present, or when the participant has a panic attack. This requires continual prospective monitoring and results in a high workload for participants. The recorded event initiates a questionnaire. Events should be discrete (have a clear beginning and end) and often require coding instructions (what is a panic attack or a social interaction?). There is no correction for rating misinterpretation. Feasibility limits the number of (different) events that can be reported. Having to respond to additional questions after reporting an event can act as a ‘punishment’ and results in an extinction of the report (not the actual event) under some (stressful) circumstances. The same ‘learning curve’ can occur in branching when different answers lead to different workloads. Often, time sampling is preferred because it is less burdensome, more reliable, allows the (non-exhaustive) assessment of a larger set of events and reports events as well as non-events (eg, when a participant smokes, as well as when a participant does not smoke).
When beeps are programmed at fixed times, predictability increases reactivity and this may induce behavioural changes (eg, postpone shopping or showering). More generally, when beep-free periods can be anticipated (eg, no beep expected within an hour after a beep), reactivity increases. Random sampling avoids this reactivity. Unexpectedly, true random schedules (eg, 3 beeps on 1 day and 15 on another) are not ideal, because long periods with no beeps can result in some participants staying at home (not to miss a beep). This behaviour disrupts normal daily life. Therefore, a stratified random schedule with restricted intervals is advised.4
This list of design modalities is not exhaustive and the actual choices depend on the research question and study population.
Verhagen S.J., Hasmi L., Drukker M., van Os J, & Delespaul P.A. (2016). Use of the experience sampling method in the context of clinical trials. Evidence-Based Mental Health, 19(3), 86-89.
Publication 2016
Anxiety Extinction, Psychological Learning Curve Mood Panic Attacks Reactive Depression Reading Frames Respiratory Diaphragm Smoke

Most recents protocols related to «Learning Curve»

1
Evaluating Surgical Learning Curves using CUSUM
The cumulative sum (CUSUM) method was used for quantitative assessment of the learning curve; this is the cumulative sum of differences between the individual data points and the mean of all data points. The CUSUM method enables the detection of small changes in performance measures that may be undetectable using other measures (11 (link),12 (link)). The CUSUM for the variables of interest in the first patients was the difference between the value for the first patient and the mean for all patients. The CUSUM for the second patient was the previous patient’s CUSUM added to the difference obtained for the second patient. This recursive process continued until the CUSUM for the last patient was calculated as zero. In this study, the learning curve was evaluated using operative time and CUSUM (CUSUMOT). We assessed the curve of best fit for detecting the change in slope of the CUSUM learning curve. In this method, positive and negative slopes indicated a series of cases with above-average and below-average operative time, respectively. The cases required for learning were calculated from the inflection point of the curve of the line representing the best fit for the plot.
Igai H., Matsuura N., Numajiri K., Ohsawa F, & Kamiyoshihara M. (2023). Supervision by an experienced surgeon can reduce the learning curve of uniportal thoracoscopic lobectomy. Translational Lung Cancer Research, 12(2), 207-218.
Publication 2023
Learning Curve Patients
2
Uniportal Thoracoscopic Lobectomy Learning Curve
This retrospective study was performed to investigate the learning curve of uniportal thoracoscopic lobectomy with ND2a-1 or greater lymphadenectomy for two senior surgeons (HI and NM) in our department, and to evaluate how supervision affected the learning curve. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study protocol was approved by institutional ethics board of Japanese Red Cross Maebashi Hospital (approval No. 2022-05) and the need for individual consent for this retrospective analysis was waived.
Our department began uniportal thoracoscopic major pulmonary resection including lobectomy and segmentectomy in February 2019. The uniportal approach was initially adopted only for cT1N0 cases or metastatic lung cancer to ensure the safety of the operation; this was also part of our strategy to allow the surgical team to become familiar with this less invasive procedure. A multiportal approach was adopted for all other major pulmonary resections during this introductory period. All of the initial 30 operations were performed by HI. In December 2019, after uniportal thoracoscopic major pulmonary resection had been performed in 30 cases, the uniportal approach was adopted for most patients with primary lung cancer. The exclusion criteria were as follows: requiring angioplasty and/or bronchoplasty, necessary to reconstruct the chest wall, invasion into intrathoracic great vessels, or tumor measuring ≥7 cm. After HI had performed uniportal thoracoscopic major pulmonary resection in 40 cases, NM and junior surgeons began to perform this operation under supervision by HI. Both HI and NM had performed more than 500 thoracoscopic major pulmonary resections via a multiportal approach before starting the uniportal approach. Although the experience varied among the junior surgeons, any of them had less than 50 thoracoscopic major pulmonary resections via a multiportal approach. In addition, when the junior surgeons encountered the technically difficult part, the experienced surgeon (HI) alternatively defeated it.
Between February 2019 and January 2022, 259 patients underwent thoracoscopic major pulmonary resection in our department. Among them, 140 patients with lung cancer undergoing uniportal thoracoscopic lobectomy with ND2a-1 or greater lymphadenectomy were enrolled in this study. Figure 1 presents the patient enrollment process. The clinical data analyzed for each case included age, sex, American Society of Anesthesiologists (ASA) score, smoking index (pack-years), forced expiratory volume in one second (FEV1.0), %FEV1.0, tumor localization, histology, clinical stage, operative time, intraoperative blood loss, rate of significant vessel injury, rate of conversion to thoracotomy, duration of postoperative drainage, postoperative hospitalization time, morbidity (Clavien-Dindo grade ≥ III), rate of readmission within 30 days after the operation, and 30- and 90-day postoperative mortality rates.
Igai H., Matsuura N., Numajiri K., Ohsawa F, & Kamiyoshihara M. (2023). Supervision by an experienced surgeon can reduce the learning curve of uniportal thoracoscopic lobectomy. Translational Lung Cancer Research, 12(2), 207-218.
Publication 2023
Anesthesiologist Angioplasty Blood Vessel Drainage Hospitalization Japanese Learning Curve Lung Lung Cancer Lymph Node Excision Neoplasms Patients Safety Segmental Mastectomy Supervision Surgeons Surgery, Day Surgical Blood Losses Thoracoscopes Thoracotomy Vascular System Injuries Volumes, Forced Expiratory Wall, Chest
3
Optimized DCNN Training for Medical Image Analysis
The runtime environment for DCNN training and classification was as follows: OS: Microsoft Windows 10; CPU: Intel Core i7-1070; GPU: NVIDIA Quadro RTX6000; Memory: 64 GB. The MathWorks MATLAB 2021a, an integrated development environment, was used for each process [15 ]. For DCNN, a pre-trained ResNet101 [16 ] was used for fine-tuning. The fully connected layer and the classification layer were re-layered to correspond to the classification of this study. ResNet101, which had already been trained by ImageNet, was used in this study [17 ]. The best performance for region extraction and object detection in medical images using DCNN is expected when all layers are re-trained [18 (link), 19 (link)]. In this study, all layers were re-trained during fine-tuning. In addition, the best accuracy among other architecture selections and parameter setting were applied in this study. The parameters were set to a learning rate of 0.001, a batch size of 64, and 10 epochs. Stochastic gradient descent with momentum as an optimization algorithm was employed. In this study, the learning curve was observed to confirm that overfitting did not occur and that were learned adequately.
Nakayama Y., Sato M., Okamoto M., Kondo Y., Tamura M., Minagawa Y., Uchiyama M, & Horii Y. (2023). Deep learning-based classification of adequate sonographic images for self-diagnosing deep vein thrombosis. PLOS ONE, 18(3), e0282747.
Publication 2023
EPOCH protocol Learning Curve Memory
4
Predictive Capacity of J-CTO Score
Continuous variables were presented as mean ± standard deviation. Categorical variables were expressed as absolute numbers and percentages. The Student t statistic was used to compare quantitative variables between two groups and χ2 test was applied to evaluate the association between qualitative variables. The level of interobserver concordance regarding the measurement of J-CTO variables was assessed by Kappa index statistic. Multivariate model was constructed with variables included in J-CTO score and the predictive capacity of the model was determined with multivariate logistic regression. The goodness-of-fit of the model was assessed with Hosmer and Lemeshow (HL) test so as to evaluate any possible discrepancy between observed and expected values. Subsequently the discriminatory power of the logistic model was estimated by the area under the receiver operator characteristic curve (ROC) or C/index. An additional analysis of two periods represented by cases 1 to 200 (first block) and cases 201 to 526 (second block) was performed in order to assess the potential influence of learning curve in modification of the predictive capacity of the model.
All analyses were performed using the statistical package of SPSS 19 and the p value of < 0.05 was considered statistically significant.
Mohandes M., Moreno C., Rojas S., Doblas V., Fuertes M., Fernández F., Pernigotti A., Guarinos J., Camprubi M, & Bardají A. (2023). J-chronic total occlusion score predictive capacity for percutaneous coronary intervention success of chronic total occlusion: Results from a European single center cohort with progressive experience over time. Cardiology Journal, 30(1), 59-67.
Publication 2023
Learning Curve Student
5
Contralateral Crossing Technique for CTO Intervention
Contralateral injection using double access was used in case any hetero collateral was found in the diagnostic angiogram. Dedicated wires and microcatheters for crossing the CTO segment were used in all procedures and the segment before the occlusion was traversed with a floppy wire in order to minimize any damage to the artery. Anterograde approach was the preferred strategy during the initial steps of our learning curve and retrograde approach was incorporated later and was used either after failing the antegrade approach or as an initial strategy. In all cases activated clothing time was checked every 30 min to keep a level of 250–300 s for antegrade and between 300–350 s for retrograde access.
Mohandes M., Moreno C., Rojas S., Doblas V., Fuertes M., Fernández F., Pernigotti A., Guarinos J., Camprubi M, & Bardají A. (2023). J-chronic total occlusion score predictive capacity for percutaneous coronary intervention success of chronic total occlusion: Results from a European single center cohort with progressive experience over time. Cardiology Journal, 30(1), 59-67.
Publication 2023
A 300 Angiography Arteries Dental Occlusion Diagnosis Learning Curve

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More about "Learning Curve"

The learning curve, also known as the performance curve or skill acquisition curve, refers to the gradual improvement in an individual's or group's proficiency over time as they engage in a specific task or activity.
This non-linear relationship between experience/practice and the level of expertise attained is influenced by factors such as task complexity, learner motivation, and the effectiveness of teaching or training methods.
Understanding the learning curve is crucial in fields like education, skills development, and human-computer interaction, as it helps optimize learning strategies and predict the time and effort required to master new skills.
Researchers have explored learning curves using tools like Prism 6, GraphPad Prism 5, Prism 8, SAS 9.4, MATLAB, Stata 12.0, SPSS Statistics, SPSS version 25, and SPSS version 23, providing insights into the cognitive processes underlying skill acquisition.
Mastering the learning curve is essential for achieving reproducible and accurate results in AI-driven research protocol optimization, as it allows researchers to locate the best protocols from literature, pre-prints, and patents using intelligent comparisons.
By understanding the learning curve, researchers can design more effective learning environments and optimize their research protocols for unparalleled success.
One key aspect of the learning curve is the gradual improvement in performance or skill acquisition over time.
This non-lineal relationsip between experience/practice and proficiency can be influenced by a variety of factors, such as task complexity, learner motivation, and the effectiveness of teaching or training methods.
Reserach on learning curves can provide valuable insights into the cognitive processes underlying skill acquisition, ultimately helping to design more effective learning environments.