Omission Errors Research Articles

A Smart Procedure for Assessing the Health Status of Terrestrial Habitats in Protected Areas: The Case of the Natura 2000 Ecological Network in Basilicata (Southern Italy)

Natura 2000 is the largest coordinated network of protected areas in the world, which has been established to preserve rare habitats and threatened species at the European Community level. Generally, tools for habitat quality assessment are based on the analyses of land-use/land-cover changes, thus, highlighting already overt habitat modifications. To evaluate the general quality conditions of terrestrial habitats and detect habitat degradation processes at an early stage, a direct and cost-effective procedure based on satellite imagery (Landsat data) and GIS (Geographic Information System) tools is proposed. It focuses on the detection of anomalies in vegetation matrix (stress/fragmentation), estimated for each habitat at the level of both a single protected site and local network, to identify habitat priority areas (HPA), i.e., areas needing priority interventions, and to support a rational use of resources (field surveys, recovery actions). By analyzing the statistical distributions of standardized NDVI for all the enclosed habitats (at the site or network level), the Degree of Habitat Consistency (DHC) was also defined. The index allows the assessment of the general status of a protected site/network, and the comparison of the environmental conditions of a certain habitat within a given protected site (SCI, SAC) with those belonging to the other sites of the network. The procedure was tested over the Natura 2000 network of the Basilicata region (Southern Italy), considered as a hotspot of great natural and landscape interest. An overall accuracy of ~97% was obtained, with quite low percentages of commission (~8%) and omission (~6%) errors. By examining the diachronic evolution (1985–2009) of DHC and HPA, it was possible to track progress or degradation of the analyzed areas over time and to recognize the efficaciousness/failure of past managements and interventions (e.g., controlled disturbances), providing decision-makers with a thorough understanding for setting up the most suitable mitigation/contrast measures.

Diferencias atencionales entre el trastorno por déficit de atención aislado y asociado a neurofibromatosis tipo 1

IntroductionThe incidence of ADHD in patients with NF1 reaches 60%. Beyond the published literature on cognitive aspects in subjects with NF1, some studies suggest that the attentional profile of patients with NF1+ADHD is different from those with isolated ADHD. MethodsA retrospective, analytical case-control study with the objective of comparing the intellectual and attentional profile in children with NF1+ADHD, NF1 and isolated ADHD in order to characterise them, noting similarities and differences. ResultsNeuropsychological evaluations from 2017 to 2021 were reviewed for all subjects with a diagnosis of NF1 (n=46) with IQ>70 and ADHD (n=180). In the ANOVA test, it was observed that patients with NF1+ADHD presented with greater visuospatial impairment (M=84.66; F(2,210)=7.84; P<0.001; η2=0.07) and a lower total intellectual level (M=84.20; F(2,223)=4.35; P=0.01; η2=0.04). In the attentional tests they showed more errors of omission (M=72.66; F(2,223)=18.13; P<.001; η2=0.14) and of commission (M=57.87; F(2,223)=4.68; P=0.01; η2=0.04) and lower reaction speed (M=66.58; F(2,223)=19.24; P<0.001; η2=0.15) compared to patients with NF1 and isolated ADHD. ConclusionsAttention deficit in patients with NF1 is associated with lower performance in intellectual functioning and greater visuospatial impairment. The attentional pattern of patients with NF1+ADHD was different from those with isolated ADHD; the difference being not only quantitative but also qualitative, supporting the hypothesis that attention impairment has its own features of NF1 and independent of ADHD.

Novel Approach Using Risk Analysis Component to Continuously Update Collaborative Robotics Applications in the Smart, Connected Factory Model

Building on the idea of Industry 4.0, new models of the highly connected factory that leverage factory-generated data to introduce cost-effective automation and involve the human worker for creating higher added value are possible. Within this context, collaborative robots are becoming more common in industry. However, promises regarding flexibility cannot be satisfied due to the challenging process of ensuring human safety. This is because current regulations and standards require updates to the risk assessment for every change to the robotic application, including the parts involved, the robotic components, and the type of interaction within the workspace. This work presents a novel risk analysis software tool that was developed to support change management for adaptive collaborative robotic systems in the connected factory model. The main innovation of this work is the tool’s ability to automatically identify where changes have been made to components or processes within a specific application through its integration with a connected factory architecture. This allows a safety expert to easily see where updates to the risk assessment are required, helping them to maintain conformity with the CE marking process despite frequent changes. To evaluate the benefits of this tool, a user study was performed with an exemplary use-case from the SHOP4CF project. The results show that this newly developed technology for risk assessment has better usability and lower omission errors when compared to existing methods. Therefore, this study underlines the need for tools that can help safety engineers cope with changes in flexible robotics applications and reduce omission errors.

Virtual reality-based and eye tracking-assisted attention refocusing training for adult Attention-Deficit/Hyperactivity Disorder

IntroductionNeurofeedback regimes in the treatment of adult ADHD are commonly EEG-based and have several shortcomings, including a weak signal-to-noise ratio, low transfer rates from laboratory to everyday environments and ambiguous evidence in respect to adequate brain signals of interest.ObjectivesTo investigate, if an eyetracking-based real-time feedback in a virtual environment can enhance attentional performance, as measured by behavioral, EEG and eyetracking parameters.MethodsOverall, n=18 adult patients with ADHD and n=18 healthy controls (HC) performed a continuous performance task (CPT) in a virtual seminar room, while distracting virtual events occurred. In case the participant’s gaze drifted away from the task an automated audiovisual feedback indicated the participant to refocus on the task. Three 20-minutes blocks were presented in counter-balanced order, that differed in respect to whether real feedback, sham feedback or no feedback was additionally provided.ResultsMixed ANOVAs with within-subject factors ‘Condition’ (real feedback, sham feedback, no feedback) and ‘Phase’ (distractor phases vs. non-distractor phases) and a between-factor ‘Group’ (ADHD patients vs. HC) revealed better task performances in HC than ADHD patients in respect to omission errors (p = .023), mean reaction times (p = .042) and reaction time variabilities (p = .007; cf. Figure 1). Moreover, omission errors turned to be higher during distractor-present than distractor-absent trials (p = .007), especially in ADHD.Figure 1.CPT results. DP=distractor-phases, NDP=non-distractor-phasesConclusionsWhile the virtual CPT turns out to discriminate well between patients with ADHD and HC, the behavioral results do not indicate an attentional performance enhancement based on the gaze-dependent feedback.DisclosureNo significant relationships.

Does sport type matter? The effect of sport discipline on cognitive control strategies in preadolescents

This study aimed to test the effects of specific sport practices on cognitive sensory-motor performance and underlying brain functions in children. Behavioral performance and event-related potentials (ERP) were investigated during a cognitive visuomotor task in 64 preadolescent children practicing racket (Rack) sports, martial arts (Mart), indoor climbing (Clim), or not practicing any sport (controls, Cont). At the behavioral level, response speed and accuracy were studied. At the electrophysiological level, motor, cognitive and sensory-attentional readiness, and post-perceptual attentional functions were investigated. Behavioral results showed that Mart players had the fastest response time (RT). Rack players had the most consistent RT and committed the lowest omission errors. Clim athletes were the most accurate in terms of false alarms. ERP results showed that motor readiness was largest in Mart players. The Rack group had the largest cognitive preparation and the Clim one had the largest sensory-attentional readiness activity. Rack and Mart players had the largest activity associated with post-perceptual attentional processing. This result shows that practicing specific sports may allow differential benefits on cognitive processing. Racket sports seem to stimulate action speed consistency and improve accuracy for omissions, increasing cognitive preparation, and post-perceptual attentional processing. Mart practice may allow a more speed-oriented response behavior, probably due to large motor preparation and allocation of post-perceptual attentional resources, but only when response execution is required. Indoor climbing may favor response accuracy reducing unwanted responses as indexed by an increased sensory-attentional readiness. Overall, all the considered sports disciplines may improve cognitive processing, but each one is associated with different benefits on cognitive performance by possibly stimulating separate brain processing. This kind of information could be crucial to select the more appropriate sport depending on individual demands.

AN ANALYSIS OF STUDENTS’ ERROR IN TRANSLATING INDONESIAN SENTENCES INTO ENGLISH

This research was an error analysis of the first semester students of Sumbawa University of Technology. The aims of the research were to find out what types of error that the students committed and what error that they committed the most. The research was a descriptive study, where the population of the research was all first semester students, while the sample was Electrical Engineering Class which consists of 34 students. The technique data collection was by giving the students translation tests. The data was analyzed by identifying, classifying, and describing the errors. Based on the result of analysis, it was found that (1) there were four types of error that students committed, they were omission, addition, misordering and misformation error: (2) type of error that students committed the most was omission, and the least error was miordering.

IMPROVED ON SNOW COVER EXTRACTION IN MOUNTAINOUS AREAS BASED ON MULTI-FACTOR NDSI DYNAMIC THRESHOLD

Abstract. Snow cover is one of the most active elements of the cryosphere and plays an important role in the surface radiation budget and water balance. Optical satellite remote sensing has become an important tool for snow identification and monitoring. The Sentinel-2 A/B satellite has become an important data source for snow cover extraction because of its high radiation resolution, which reduces the problem of snow/ice saturation in remote sensing observations. The normalized differential snow cover index (NDSI) and the snow cover index considering the forest cover area (NDFSI) are important methods for snow cover extraction, but due to the strong spatial heterogeneity and fast change speed of snow cover in mountainous areas, using the classical fixed threshold method to extract snow cover will result in a large omission error. In this paper, a dynamic threshold method is constructed by synthesizing the effects of the type of snow cover period, aspect and snow underlying land coverage type on the snow cover NDSI/NDFSI. Compared with the high-resolution GF-2 snow map results, the dynamic threshold method has higher accuracy in extracting snow cover, and the overall classification accuracy, omission error, commission error and Kappa coefficient are 97.70%, 0.20%, 11.17% and 0.93 respectively. The dynamic threshold method is used to extract snow cover in a snow cover period in the Babao River Basin. The snow cover rate in the basin fluctuates greatly with time, and the spatial distribution of snow cover is uneven, with more snow cover in mountainous areas and rapid changes in snow cover in river valleys.

Child sex differences in the auditory equiprobable Go/NoGo task

Our previous studies of the equiprobable auditory Go/NoGo task have been used to substantiate a perceptual/cognitive Processing Schema in young and older adults, and in children. The processes in the Schema are linked to PCA components derived separately from Go and NoGo ERPs. Here we investigated sex differences in the child Schema. Two groups of fourteen children (aged 8 to 13 years) were individually matched on age and presented with four stimulus blocks of the equiprobable Go/NoGo task, each containing 75 NoGo and 75 Go tones in random order. Separate NoGo and Go ERPs were obtained from each child and submitted to temporal Principal Components Analyses (PCAs). Each ERP was analysed in two epochs (−100–400 ms and 300–800 ms) to improve the cases:variables ratio. Four pairs of temporal PCAs, each with unrestricted VARIMAX rotation, separately quantified the NoGo and Go ERPs of each epoch in each group. After these pairs were combined in temporal order, four sets of similar components were extracted. Many identified components were differentially enhanced to either NoGo or Go, as in previous work with children. The Female group had NoGo component latencies that were systematically some 3.5% greater than in Males, but there were no sex differences in Go latencies. Females also displayed fewer NoGo commission and Go omission errors, and faster Go RT than Males. Females had larger NoGo N2b, and larger Go components from N2b through to the late positivity. These results, including their ERP component/behaviour correlations, can be integrated as a task-specific behavioural and ERP processing enhancement in girls that suggests their developmental advantage over boys in this age range.

AN ANALYSIS OF SYNTAX ERRORS IN DESCRIPTIVE ESSAYS

This research was conducted to analyze the syntax error. The object of this research is a descriptive essay for English education students batch 2019 which was made during the second semester. The author's aim in this study is to analyze the existence of syntax errors and the types of errors in writing descriptive essays. In this study, the author uses a qualitative descriptive method. The results of the research in the findings and discussion section stated that the descriptive essays of English education students in class 2019 stated that there were a number of syntax errors. In the finding section, the writer found eleven types of syntax error sentences in three student descriptive essays. From eleven example sentences, the writer finds the location of errors in the structure of phrases, clauses, and sentences. And from the three error syntax areas, two types of syntax errors were found by students, there are omission and addition. The most syntax errors are phrase structure errors in omission and the smallest is clause errors in the ‘Wh’ question. Many sentence errors in omission because incorrect because there is no verb in the clause. Each clause needs a subject and a verb. In some cases, the best verb to use is a simple is or was (or some other form of the verb to be). Keywords: error analysis, syntax error, descriptive essay.

K-MEANS CLUSTERING BASED ON OMNIVARIANCE ATTRIBUTE FOR BUILDING DETECTION FROM AIRBORNE LIDAR DATA

Abstract. Building detection is an important process in urban applications. In the last decades, 3D point clouds derived from airborne LiDAR have been widely explored. In this paper, we propose a building detection method based on K-means clustering and the omnivariance attribute derived from eigenvalues. The main contributions lie on the automatic detection without the need for training and optimal neighborhood definition for local attribute estimation. Additionally, one refinement step based on mathematical morphology (MM) operators to minimize the classification errors (commission and omission errors) is proposed. The experiments were conducted in three study areas. In general, the results indicated the potential of proposed method, presenting an average Fscore around 97%.

ACCURACY ASSESSMENT OF CLOUD MASK DETECTION ALGORITHMS FOR CBERS-4 WFI IMAGERY

Abstract. Clouds limit the potential use of optical images. Proper clouds and cloud shadows detection are necessary steps for optical image applications. Few algorithms are flexible in detecting clouds in images with a limited number of bands, such as the Wide-Field Imager (WFI) sensor on board the China-Brazil Earth Resources Satellite (CBERS-4), which has four spectral bands (blue, green, red, and near-infrared). Therefore, this work aims to assess the accuracy of two cloud detection algorithms: CMASK and ATSA, and evaluate the influence of the ATSA configuration parameters. We selected four regions in Brazil for our analysis. In all cases, ATSA presented overall accuracy (OA) superior to CMASK. While the ATSA obtained OA greater than 0.91 for all analyzes, the OA from CMASK did not exceed 0.84. CMASK presented commission errors for the No Clear class (combination of Cloud and Cloud shadow) and inclusion errors for the Clear class close to zero. However, many errors of omission of clouds misclassified as the Clear class was observed. The ATSA algorithm presented a better balance between inclusion errors and omission errors. Our results can be used as guidance for choosing a cloud mask algorithm for the CBERS-4 WFI images and for analysis considering the images from WFI on board CBERS-4A and Amazonia-1, as they have similar characteristics.

UAV-Borne Imagery Can Supplement Airborne Lidar in the Precise Description of Dynamically Changing Shrubland Woody Vegetation

Airborne laser scanning (ALS) is increasingly used for detailed vegetation structure mapping; however, there are many local-scale applications where it is economically ineffective or unfeasible from the temporal perspective. Unmanned aerial vehicles (UAVs) or airborne imagery (AImg) appear to be promising alternatives, but only a few studies have examined this assumption outside economically exploited areas (forests, orchards, etc.). The main aim of this study was to compare the usability of normalized digital surface models (nDSMs) photogrammetrically derived from UAV-borne and airborne imagery to those derived from low- (1–2 pts/m2) and high-density (ca. 20 pts/m2) ALS-scanning for the precise local-scale modelling of woody vegetation structures (the number and height of trees/shrubs) across six dynamically changing shrubland sites. The success of the detection of woody plant tops was initially almost 100% for UAV-based models; however, deeper analysis revealed that this was due to the fact that omission and commission errors were approximately equal and the real accuracy was approx. 70% for UAV-based models compared to 95.8% for the high-density ALS model. The percentage mean absolute errors (%MAE) of shrub/tree heights derived from UAV data ranged between 12.2 and 23.7%, and AImg height accuracy was relatively lower (%MAE: 21.4–47.4). Combining UAV-borne or AImg-based digital surface models (DSM) with ALS-based digital terrain models (DTMs) significantly improved the nDSM height accuracy (%MAE: 9.4–13.5 and 12.2–25.0, respectively) but failed to significantly improve the detection of the number of individual shrubs/trees. The height accuracy and detection success using low- or high-density ALS did not differ. Therefore, we conclude that UAV-borne imagery has the potential to replace custom ALS in specific local-scale applications, especially at dynamically changing sites where repeated ALS is costly, and the combination of such data with (albeit outdated and sparse) ALS-based digital terrain models can further improve the success of the use of such data.

Grammatical Errors Found in Articles' Abstracts of Indonesian Scholarly Journals

This study is aimed to know the grammatical errors found in the articles’ abstracts of scholarly journals published by one of Indonesian Islamic State Colleges in 2008-2010. The theory used to analyze the data in this case study is Burt and Kiparsky’s theory, namely Surface Strategy Taxonomy. This theory devides errors into errors of omission,errors of addition, errors of misformation and errors of misordering. This results of the study show that there are 172 items of grammatical errors. The most frequent type of grammatical error is omission with the total number is 72 items or 41.9%. The second is errors of misformation which consist of 57 items or 33.1%. The next is errors of addition (27 items or 15.7%) and finally is errors of misordering as the least number of errors with 16 items or 9.3%.

The effect of administration skills on Rorschach protocols

Although the Rorschach is the most widely studied and used non self-report measure to assess personality, no empirical studies have been carried out to investigate what level of expertise is needed to properly collect valid Rorschach protocols.The aim of the current study was to explore, using a within-subject design, the effect of training on proficiency in administering the Rorschach, specifically a) what kind of errors are associated with different levels of training, and b) what level of expertise guarantees a sufficiently effective Rorschach administration.The second (T1), the fifth (T2) and the tenth (T3) Rorschach protocols collected by 50 licensed psychologists attending a Rorschach training course were examined to explore what kind of mistakes were made at the different stages of training.In the Response phase, examiners collected more simplistic protocols and made more errors at T1, while they showed satisfactory proficiency at T2. Regarding the Inquiry Phase, the most relevant finding was that at T1 examiners made both errors of omission and commission, while at T2 they no longer made errors of omission but they did make more errors of commission, likely as a temporary effect of the training. At T3 a sufficiently effective Rorschach administration was carried out. Findings suggest that a 100-h Rorschach training course and 10 administrations as practicum, each followed by individual feedback and group discussion, might be sufficient to guarantee sufficiently effective Rorschach administration skills.

A new method for surface water extraction using multi-temporal Landsat 8 images based on maximum entropy model

ABSTRACT The spectral matching algorithm based on the discrete particle swarm optimization algorithm (SMDPSO) sometimes overestimates extracted surface water areas. Here we constructed a new method (MEDPSO) by coupling discrete particle swarm optimization algorithm with maximum entropy model (MaxEnt) to extract water bodies using Landsat 8 Operational Land Imager (OLI) images. To compare the accuracy of the modified normalized difference water index (MNDWI), SMDPSO, and MEDPSO, we selected six areas , i.e. thermokarst lakes, Coongie Lakes National Park, the Amazon River, urban water bodies mixed with buildings, Erhai Lake that is surrounded by mountains, and high-altitude lakes. Our results show that the average overall accuracy of the MEDPSO for the six areas is 97.4%, which is higher than those of MNDWI and SMDPSO. The average commission errors and omission errors of MEDPSO (6.4% and 0.8%) are lower than those of MNDWI and SMDPSO. The MEDPSO has a higher accuracy because the maximum entropy model is a machine learning method that uses all the bands of Landsat imagery and four surface water indices in the calculation of the probability of surface water. Our study established a novel, high-precision water extraction method.

Monitoring coastal water flow dynamics using sub-daily high-resolution SkySat satellite and UAV-based imagery

Sub-daily tracking of dynamic features and events using high spatial resolution satellite imagery has only recently become possible, with advanced observational capabilities now available through tasking of satellite constellations. Here, we provide a first of its kind demonstration of using sub-daily 0.50 m resolution SkySat imagery to track coastal water flows, combining these data with object-based detection and a machine-learning approach to map the extent and concentration of two dye plumes. Coincident high-frequency unmanned aerial vehicle (UAV) imagery was also employed for quantitative modeling of dye concentration and evaluation of the sub-daily satellite-based dye tracking. Our results show that sub-daily SkySat imagery can track dye plume extent with low omission (8.73–16.05%) and commission errors (0.32–2.77%) and model dye concentration (coefficient of determination = 0.73; root mean square error = 28.68 ppb) with the assistance of high-frequency UAV data. The results also demonstrate the capabilities of using UAV imagery for scaling between field data and satellite imagery for tracking coastal water flow dynamics. This research has implications for monitoring of water flows and nutrient or pollution exchange, and it also demonstrates the capabilities of higher temporal resolution satellite data for delivering further insights into dynamic processes of coastal systems.

Compensation Claims in Danish Emergency Care: Identifying Hot Spots and Blind Spots in the Quality of Care.

The Healthcare Complaints Analysis Tool (HCAT) offers a validated way of systematically extracting content from patient complaints for further analysis of complaint hot spots with harm or near misses, and blind spots with, for example, systemic problems or quality problems arising during discharge. This study analyzed a Danish national sample of compensations claims about emergency care using the HCAT. Through use of the HCAT, compensation claims about Danish emergency care from 2013 to 2017 (N = 712) were coded and then grouped to identify and highlight hot spot problem areas (harm and near misses) and blind spot problem areas (admission/discharge, systemic problems, errors of omission). Two assessors coded the compensation claims by entering data into a database. The HCAT analyses of the sample resulted in coding of 1,305 problems. Most problems concerned quality and safety issues at the examination/diagnosis stage of care (63.9%). In 91.2% of the cases, the level of harm was moderate or major. Harm hot spots most often involved diagnostic errors (189 problems). Eighty-nine problems related to errors of omission, all causing moderate or major harm. For systemic blind spots, patient harm significantly increased in cases of multiple problem types in the compensation claim (odds ratio = 1.6, 95% confidence interval = 1.3-2.0). Systematic coding and analytic approach to the HCAT can highlight potential quality problems in emergency care and point to areas for further consideration. From the perspective of future health care harm prevention, there seems to be a strong incentive for further analysis of the amount, nature, and prevention of diagnostic errors in emergency care.

Grammatical Errors Committed by Students of Mts Nurul Huda Sawo in Writing Descriptive Texts

This research was done to investigate the grammatical errors on descriptive texts written by the learners. Grammatical errors were analyzed based on Dulay, Burt, and Krashen’s surface strategy taxonomy. It consisted of four types of grammatical errors, namely omission, addition, misformation, and misordering. 30 students of the eighth grade students of MTs. Nurul Huda were the subjects of this research. The aims of this research were to know the kinds of grammatical errors and the grammatical error mostly committed by the students in writing descriptive texts. Qualitative was chosen as the research design. Analysis and writing test result were used as the instruments of the data collection. The result of this research indicated that there were 67 grammatical errors committed by the students. Misformation error was the highest percentage with 57.98%. Followed by Omission error was with 34.79%. Addition error was in the third place with 4.35%. While, the lowest mistake that the learners made was Misordering error (2.88%). Based on the findings, the teacher should pay attention to the students’ mistakes, especially on grammatical errors and push the learners to learn more about grammar.

Conflict- and error-related theta activities are coupled to BOLD signals in different brain regions

Both conflict and error processing have been linked to the midfrontal theta power (4–8 Hz) increase as indicated by EEG studies and greater hemodynamic activity in the anterior midcingulate cortex (aMCC) as indicated by fMRI studies. Conveniently, the source of the midfrontal theta power was estimated in or nearby aMCC. However, previous studies using concurrent EEG and fMRI recordings in resting-state or other cognitive tasks observed only a negative relationship between theta power and BOLD signal in the brain regions typically showing task-related deactivations. In this study, we used a simultaneous EEG-fMRI technique to investigate a trial-by-trial coupling between theta power and hemodynamic activity during the performance of two conflict tasks. Independent component analysis (ICA) was applied to denoise the EEG signal and select individual midfrontal EEG components, whereas group ICA was applied to fMRI data to obtain a functional parcellation of the frontal cortex. Using a linear mixed-effect model, theta power was coupled with the peak of hemodynamic responses from various frontal, cingulate, and insular cortical sites to unravel the potential brain sources that contribute to conflict- and error-related theta variability. Although several brain regions exhibited conflict-related increases in hemodynamic activity, the conflict pre-response theta showed only a negative correlation to BOLD signal in the midline area 9 (MA9), a region exhibiting conflict-sensitive deactivation. Conversely, and more expectedly, error-related theta showed a positive relationship to activity in the aMCC. Our results provide novel evidence suggesting that the amplitude of pre-response theta reflects the process of active inhibition that suppresses the MA9 activity. This process is affected independently by the stimulus congruency, reaction times variance, and is susceptible to the time-on-task effect. Finally, it predicts the commitment of an omission error. Together, our findings highlight that conflict- and error-related theta oscillations represent fundamentally different processes.

Students’ Error in Transforming Active Voices into Passive Voices in Simple Past Tense

This study was carried out to analyze errors made by eleventh grade students in transforming active voices into passive voices. Specifically, it was carried out to identify common errors and obtaining the causes of errors made by the students in transforming active voices into passive voices. The research used descriptive qualitative method. The data were collected through tests and interviews with an English teacher and a number of students. The data test was identified, classified, and analyzed by using Dulay’s surface strategy taxonomy and the causes of errors were analyzed by using Petter Hubbard’s theory. The research revealed two major findings, (1) the most common errors made by students was misformation, which was most frequently produced by the students with a total error of 247 or 52%. It is followed by an error in omission with a total error of 115or 24%. Misordering with 98 errors or 20%, and the lowest is addition with 19 errors or 4%, (2) Based on Petter Hubbard’s theory, mother-tongue interferences, overgeneralization, and error was encouraged by the learning materials and methods used causing those errors.