There is a high demand for stroke patient data in the public health systems of middle and low-income countries.To develop a stroke databank for integrating clinical or functional data and benchmarks from stroke patients.This was an observational, cross-sectional, prospective study. A tool was developed to collect all clinical data during hospitalizations due to stroke, using an electronic editor of structured forms that was integrated with electronic medical records. Validation of fields in the electronic editor was programmed using a structured query language (SQL). To store the results from SQL, a virtual table was created and programmed to update daily. To develop an interface between the data and user, the Embarcadero Delphi software and the DevExpress component were used to generate the information displayed on the screen. The data were extracted from the fields of the form and also from cross-referencing of other information from the computerized system, including patients who were admitted to the stroke unit.The database was created and integrated with the hospital electronic system, thus allowing daily data collection. Quality indicators (benchmarks) were created in the database for the system to track and perform decision-making in conjunction with healthcare service managers, which resulted in improved processes and patient care after a stroke. An intelligent portal was created, in which the information referring to the patients was accessible.Based on semi-automated data collection, it was possible to create a dynamic and optimized Brazilian stroke databank. In the last 40 years, databank projects have been implemented to provide information on the clinical courses and outcomes of stroke8.Stroke is the second leading cause of death and disability worldwide. It has a significant socioeconomic impact on low- and middle-income countries, thereby affecting public health status9. However, integration of these databanks is limited to large centers and clinical trials, thus restricting access to low and middle-income countries10. In accordance with the principles of stroke care lines in developing countries, it is necessary for all quality criteria during stroke hospitalization to be registered in electronic medical records, with the aim of establishing new healthcare policies11.Implementation of a databank is important for planning and decision-making by healthcare service managers, in order to improve processes and stroke care outcomes13. Over recent years, the introduction of artificial intelligence has helped in development of databases that are integrated in networks, with the capacity to identify clinical data with greater agility and security15.Based on these premises, implementation of a computerized database system containing the main clinical indicators of stroke patients has become a reality in developing countriesTherefore, the aim of this study was to develop an interface for accessing stroke patient data and the main benchmarks in acute stroke care, in order to create a semi-automated databank for implementation in low and middle-income countries.This project with the aim of developing a semi-automated stroke databank was conducted at the stroke unit of Faculdade de Medicina de Botucatu, Botucatu, Brazil. This stroke unit has 10 beds, with an average of 30 patients/month, and belongs to a tertiary-level hospital with 684 beds. The hospital has a hospital information system (HIS) that includes electronic patient records containing clinical and treatment information. All patients who were admitted to the stroke unit were included in this project. The study was approved by the institution’s Ethics in Human Research Committee.To organize the information, the tool was designed to include initial data, a clinical summary of the presentation, hospital course (including complications and infections), investigation/complementary examinations  and discharge conditions . The rules for each subgroup were planned so as to validate the completion of certain fields, all of which were mandatory for the service to be completed.The database interface was developed on the HIS platform, and clinical data were collected based on the following benchmarks for stroke care: 1) prophylaxis for deep venous thrombosis, starting no later than the second day; 2) hospital discharge with antiplatelet therapy for patients with non-cardioembolic stroke; 3) hospital discharge with oral anticoagulation for patients with atrial fibrillation or atrial flutter; 4) use of antiplatelet agents when indicated, starting on the second day of hospitalization; 5) hospital discharge with statins for patients with atherothrombotic stroke; 6) hospital discharge with prophylactic therapy and rehabilitation plan; 7) percentage of patients with acute cerebrovascular disease; 8) length of hospital stay; 9) complications; 10) stroke type-specific ICD-10; 11) hospital mortality; 12) time to CT <25 min; and 13) door-to-needle time ≤60 min.16.To execute the development of the tool, an electronic editor of structured forms was used, which was integrated with the electronic medical record platform used in the institution. Parameterized fields and validation rules were created for essential fields. The rules were created in the field editor. Radio button components were used for information with only one choice and checkbox components were used when the information consisted of multiple choices. In addition, the validation of fields was programmed using a structured language, i.e. in structured query language (SQL)Mandatory fields were configured in the editor, and each field was parameterized. All patients who were admitted to the stroke unit were included in the database, as this was mandatory at the time of transfer to another ward or at the time of the patient’s discharge or death.To store the results from SQL, a virtual table was created and programmed to update daily at 04:00 h. The interface between the data and user was developed using the program Embarcadero Delphi, and the DevExpress component was used to generate the information displayed on the screen.The stored data were analyzed to check the data quality in accordance with the pre-established rules for creating the tool as mandatory fields with relationships between the fields. A rule was also created to identify patients who were admitted to the stroke unit and then transferred to another unit, but for whom no discharge summary was filled out at any time during hospitalization. In preparing SQL and business rules for both data analysis and for out-of-base patients, the PL/SQL  tool was used via Embarcadero. The data were extracted from the fields of the form and also from cross-referencing of other information from the computerized system, such as patients who were admitted to the stroke unit.Once extracted, the data were stored in a database, and any type of information could be consulted at any time, thus allowing cross-referencing between the data of the tool and the patients’ demographic profile. The indicators that were established according to the area and database were available in almost real time, with daily updates at 04:00 h. The database began registering patients in August 2018 and has included data from approximately 1,000 stroke patients to date.Both qualitative and quantitative results can be obtained from the detailed data included in the database. The results can be grouped to create filters that can be applied to any column within the database. This provides an overview of the total number of patients for each parameter, thus resulting in development of appropriate public health policies.In addition to the specific data characteristics of stroke patients, and owing to the integration with the electronic medical record, important clinical data such as hospital course, laboratory test results, imaging tests and surgeries, as well as the main scales used in the stroke unit, can be displayed on the screen .The quality indicators (benchmarks) were created in the database for the system to track and perform decision-making in conjunction with healthcare service managers. This resulted in improved processes and patient care after a stroke .An intelligent portal was created, in which all the information referring to patients’ care was available in a single location in a dynamic and objective manner. Development of an interface with a specific database for a hospital area enabled creation of a new concept in relation to the data that were already included in the computerized system, thus allowing new ideas to emerge in order to improve and facilitate the interpretation of large volumes of data .This study reports the development of a tool integrated with electronic medical records that is capable of recording clinical data on patients admitted to a stroke unit and identifying the main indicators of quality of care, using artificial intelligence to improve the quality of care and decision-making process.20. Zetola et al. observed the registration of patients in a database with identification, previous clinical history, family history, treatments, previous comorbidities, complementary laboratory tests, cardiac tests and neuroimaging tests. Using a computerized system, these data could be extracted dynamically, and their study suggested that a risk factor control program might lead to a reduction in stroke incidence17.Several studies using previous databases have been conducted in low and middle-income countries to identify the quality of healthcare services22.In 2013, a single database for stroke patients was established in Joinville, Brazil, via a municipal law that required all public and private healthcare establishments to forward monthly information on stroke patients to the Department of Health to create an integrated database. In 2014, the Ministry of Health coordinated a simultaneous study in five cities in Brazil to assess the incidence of, mortality due to and main risk factors for stroke. The study included individuals with stroke and determined the environmental context of stroke, using a national database of stroke patients in Latin AmericaRede Brasil AVC (Brazil Stroke Network), created to improve overall care for stroke patients in Brazil, is a non-governmental organization formed by professionals from various areas with the aim of providing quality care to stroke patients, from prevention to rehabilitation23. However, population studies to monitor clinical data on stroke patients need to be integrated into networks with shared access and periodic audits. The development of the tool in this study will help facilitate exploration of clinical data and quality indicators in low and middle-income countries.The 25.Studies on the development of systems containing specific data on stroke patients have not been previously reported. Data collection methods were not reported in previous studies that included databases. Access to information has become increasingly faster and easier through technological advances that enable implementation of structured and standardized electronic databases that use artificial intelligence. Analysis on large datasets of patient characteristics, outcomes from treatments and their costs can help identify the most clinically effective and cost-efficient treatments for a population. Models, analytics, visualization large amounts of data and use of artificial intelligence come together to offer different perspectives on healthcare challenges within the contexts of time and geography. They provide strategic solutions to assist in management, decision-making and future clinical research27.An integrated system significantly integrates separate sectors, and teamwork is recorded. This enables top management to fully analyze healthcare processes. Development of this database for stroke patients will optimize this search time and provide large quantities of relevant information for the area available, which can be used to support future studiesIn conclusion, through development of integration of this tool and electronic medical records, a dynamic and optimized stroke data bank was created. This database will be useful for managing quality indicators, assisting in the planning of actions at the stroke unit and supporting decision-making, and will serve as a basis for future studies and generation of new knowledge.


In the era of globalization and technology, society demands young generations of citizens able to work in a kind of environment characterized by complexity and diversity. Therefore, the education system faces a new kind of challenge, as graduates are reported to need transversal skills, which are unlikely to be learned through traditional classroom teaching. The overall aim of this article is to examine the needs for these skills and their acquisition by higher education teachers to cope with the evolving European labour market of the 2020s. The article envisions the importance of soft skills in the teaching profession. The empirical part consists of a comparative study with Italian and Portuguese teachers, highlighting the status of a set of crucial soft skills . Particular importance has been attached to the quality of teaching in higher education in recent years, both nationally and internationally. In the European Union, the urgent need for improvement of the quality and status of teaching and the modernization of higher education has been highlighted ,2. In thDue to this, and to the faster and unforeseen changes we have been subjected to in recent years, which have affected schools and education systems ,8, incluUntil the end of the 20th century, researchers in the field of psychology and education believed that intellectual competencies were a crucial determinant of educational and occupational achievement  However,These issues also highlight the intellectual challenge required in the contemporary science classroom, the social nature of learning, the professional identity of teachers and the acquisition of a professional language related to learning and teaching. In addition, the personal resource input of trainee teachers is increasingly seen as a key factor for effectiveness, innovation and maintaining a healthier and more committed teaching staff .Teacher competency frameworks and professional standards will define what is expected of teachers and what they are able to do, i.e., their pedagogical skills, which are considered key tools to encourage and support teacher quality . These sIn recent decades, so-called “soft skills” have started to be taken into account and they are being seen a necessary complement to traditional hard skills ,19. HardGlobalisation and technological advances bring with them new ways of educating and, consequently, new approaches and methodologies that require the development of specific skills. This context is the reason to pay more attention to soft skills education.As a novelty, this study tries to assess the personal skills of teachers  and relate them to age, gender, nationality educational sector and digital skills. The sample is made up of Italian and Portuguese teachers, whose educational systems belong to the European tradition.An exploratory descriptive cross-sectional study was carried out in two groups of teachers, one in Italy and one in Portugal, in order to know comparatively their soft and digital skills profiles.After agreeing to participate, each teacher answered the Business-focused Personality Inventory (BIP) questionnaire already adapted and used in the educational context  and receA focus group composed of experienced teachers and managers from both countries was interviewed within the research process to establish the premises that should be addressed in the discussion.All participants accepted to participate and this research respected, at all times, the requirements established in the Declaration of Helsinki of 1975.The sample is non-probabilistic and consists of 212 secondary school teachers, in particular, 106 Italian teachers (mainly from the area of Rome) and 106 Portuguese teachers (mainly from the area of Porto). The geographical distribution of the sample is due to the close collaboration between the Rome Business School and the Politechnic of Porto–Accounting and Business School (ISCAP). The Italian sample was composed of 28.3 % of women and 71.6 % of men, while the Portuguese group was composed of 64.1 % of women and 35.8 % of men. The age of the respondents in both groups was between 20 and 69 years (age M = 45.79). All participants indicated their agreement to participate in the Research. The university teachers from Rome Business School are working in a private organization; the Portuguese are from public and private polytechnic organizations.Each university teacher reported the Business-Focused Inventory of Personality [BIP] questionnaire and received a personal profile indicating his/her soft skills as feedback. This instrument aims to assess personality in the context of work, based on the theoretical bases of personalization and motivation and has been validated with a sample of 580 people so that the results pointed to a structure of three factors with alphas between 0.86 and 0.93 . QuestioThe second section corresponds to the self-assessment of soft skills. In total, the BIP questionnaire consists of 14 scales, grouped into four domains plus the impression man-agement scale. In this case, seven soft skills were self-assessed: assertiveness, networking skills, teamwork, sensitivity, action orientation, ability to work under pressure and social desirability , as is dResponses were organized on an eight-point scale ranging from “Completely true” to “Completely false”. The variables were grouped into two areas: intrapersonal and inter-personal. Very extreme scores on this scale invalidate the entire questionnaire, as they indicate that the participant has not answered truthfully. The soft skills profile obtained in the two samples through the standardized BIP questionnaire is presented and compared with the normative score of the questionnaire. The focus group composed of experienced teachers and managers built the premises to be discussed in the conclusion of this research. The qualitative data collection aimed at proposing effective soft skills improvement strategies, methodology and research procedure that have high impact on values that become functional in organizational contexts where the ability to work in groups, the communication processes aimed at change management, and the culture of innovation are crucial. The sample consisted of 212 teachers, 106 Italian and 106 Portuguese. The sample is balanced in both nationalities; however, the overall percentage is 71.6 % male in Italy and 64.1 % female in Portugal. Therefore, male teachers in Portugal and female teachers in Italy are slightly under-represented in the sample.From the data, several types of comparisons and analyses were made with respect to skills. The soft skills levels of teachers were analyzed in relation to their age, gender, nationality and public/private sector. Soft skills covered assertiveness, networking, teamwork, sensitivity, socialization, action orientation, ability to work under pressure and so-cial desirability.For the comparison between age and soft skills, the Kruskal Wallis test was used to measure the difference in the distributions of soft skills examined in relation to the age groups considered during the research. In addition, a comparison of the soft skills levels of each age group with all other age groups was carried out using Dunn’s test.The soft skills that teachers possessed were then related to their digital skills. The level of digital skills of the group of teachers was measured using the Europass Online. p-value < 0.05 was established for the analysis of statistical significance.Statistical analysis was performed with SPSS version 28 software . A As for the Italian teachers, there are no significant differences in the soft skills variable compared to the level of digital skills. Among Portuguese teachers, however, differences emerged, with older teachers being expert users (M: 79%) and basic-independent users being more able to network and more team-oriented than the Italian sample by age. p < 0.05).Regarding the skills assessed by age groups between Italian and Portuguese teachers, most of them showed a higher score in the group of Italian teachers with statistically significant differences ; except for socialization skills where a significantly higher difference was found for the Portuguese (both in the public and private sectors) and in action-orientation, with a higher score in the private sector.Social desirability showed no significant differences between Italians and Portuguese in either sector, and nor did action orientation in the public sector .Regarding sector (public/private) there are no significant differences except on Portuguese teachers on socialization skills, specifically on action-orientation skill, presenting a high score in the private sector.Finally, Italian teachers also obtained statistically significant better overall scores in most skills taking into account gender. Only socialization stood out in favor of Portuguese teachers, with significantly higher scores for both genders, as well as in the action-orientation skill, where Portuguese men also stood out in terms of their scores. In the social desirability skill, men showed no statistically significant differences between the two nationalities, as applies also to women in the action-orientation skill .This study indicates that teachers generally have high levels of interpersonal skills, despite the relatively small sample size. This may be due to or explained by the fact that teachers’ work is characterized by social interaction. The skills with the highest overall scores in this sample were assertiveness, networking, teamwork and sensitivity, with the lowest scores for both groups  being social desirability and action orientation. Perhaps teachers are increasingly in need of interpersonal skills due to the innovation and new types of creative networks emphasized in the EU modernization agenda; however, the most relevant skills mentioned in the study need to be examined closely to determine their importance in relation to the context. The comparison made between a sample of Italian and Portuguese teachers shows a fairly similar profile, although with some differences regarding certain skills: Portuguese teachers seem to be more sociable and action-oriented, while Italians show higher levels of assertiveness, networking skills, teamwork, sensitivity and ability to work under pressure.The categories of intrapersonal skills and interpersonal skills, according to Boyatzis et al. , align wThe WEF study (2018) entitled “Eight Futures of Work: Scenarios and their Implica-tions”, presents several possible visions of what the future of work could be like until 2030, stating that “(…) the evolution of learning between the current and future labour force; and the magnitude of talent mobility between geographies, are likely to influence the nature of work in the future (…)” .In addition to cultural factors, the reported differences in skill levels can be traced to the different educational systems and cultures prevailing in the two countries. Even if both countries encourage higher education for teachers, the Italian system is based on a general consistency of approach and obligatory requirements in the teacher education system, while the Portuguese system traditionally relies more on the independence of particular educational institutions, especially in higher education. It is most likely that different types of skills have gained special attention in the two systems. These factors may be visible in the official curricula, or they may be more invisible in the hidden curricula, although they are strongly connected to learning methods and processes.This study suggests that Portuguese teachers have reasons to improve their soft skills in the intrapersonal area, meaning assertiveness and aspects of teamwork; and in the interpersonal area, sensitivity and networking. Italian teachers should work on socialization and action orientation.However, it should be mentioned that the sample size does not allow for very strict conclusions, so the study must be considered within its limitations and results must be taken into account cautiously. However, several of the conclusions and suggestions presented motivate to deepen the study with larger samples and with a more homogeneous sample with respect to gender; this could generate data on soft skills that are fundamental when it comes to renewing teacher education and in-service training.It is worth highlighting the difference between skills, given the different challenges of including soft skills in teacher training and curricula in addition to the already present hard skills, which are of a totally different character. Soft skills, being related to the person and the context, can be changeable and adaptable to different situations, which gives them a subjective character and makes them difficult to measure and assess. Therefore, the scores obtained in the study regarding the rating of the level of skills used are based on a self-assessment, which does not provide as in-depth results as in the 360-degree type of assessment, but is easier to organize as a research constellation; it is necessary to create appropriate assessment frameworks for the measurement of soft skills.It is important that qualitative results integrate data derived from quantitative results and consider the complexity of the teacher’s role in 2020.Attempts are currently being made to discover the essential qualities of a good teacher around the world, although trying to put them into words is a difficult task; researchers have produced lists of competences, strongly supported by policy makers . Some reStakeholder involvement can help ensure that standards are owned by the profession rather than imposed as a top-down approach to teacher accountability ; at the Competency frameworks and professional standards define what teachers are able to do and what they should know how to do, avoiding the methodological challenges of measuring teacher quality with “proxy measures” such as years of professional experience or student performance. These measures do not report on support for student development or the ability to create learning opportunities.  CompetenThe educational contexts are changing accordingly. They require skills in multi-channel communication, facilitating interaction, flexibility and use of different tools . There are calls for the adoption of open innovation mindset, framework 4.0, social technologies, digital strategies, and other enabling technologies .Therefore, the teacher education curriculum process obviously needs to be able to provide relevant knowledge and skills, as well as conceptual and soft skills in addition to hard skills. This inclusion of soft skills within the curriculum can ensure the success of the profession in the 2020s, but in order for teachers to be able to adapt to innovations, changes and be informed about developments in education, they should be prepared for professional development as educators throughout their careers. To this end, in-service training courses (INSET) have an essential role to play in teaching teachers about innovations and changes with the aim of facilitating change . These online programs are being organized as an empowering solution to ensure teachers’ professional development in this era of digitalization ; althougReviews of the development of instruction in higher education ,47 have As a limitation, it is worth mentioning that the changes have been measured by interpretations of the various questions, and may not reflect the performance of the teachers who have been surveyed. However, previous research mentions the fact that conceptions can be related to approaches , so it mTeaching is a complex act, requiring a wide range of knowledge and skills to success-fully manage the demands of the classroom ,51. TherThe teaching curriculum that has been proposed in each new reform has been introducing novel topics that increasingly open the way for school practice and explicitly for educational research; but the division of time, knowledge and space still remains unexplored. In order to achieve these fundamental changes, a curriculum-centered teaching and learning of soft skills, using them as concrete methodological approaches and not as abstract pedagogical guidelines, has been promised as a vehicle. This approach therefore highlights the importance of pedagogy as a strategic point in the education system.As a final conclusion, it is important to consider the incorporation of hard and soft skills courses in the teaching curriculum in order to motivate teachers and improve the values of humanistic pedagogy, which would promote the pedagogical culture and generate forms of continuous learning.


In recent years, the ability of intelligent systems to be understood by developers and users has received growing attention. This holds in particular for social robots, which are supposed to act autonomously in the vicinity of human users and are known to raise peculiar, often unrealistic attributions and expectations. However, explainable models that, on the one hand, allow a robot to generate lively and autonomous behavior and, on the other, enable it to provide human-compatible explanations for this behavior are missing. In order to develop such a self-explaining autonomous social robot, we have equipped a robot with own needs that autonomously trigger intentions and proactive behavior, and form the basis for understandable self-explanations. Previous research has shown that undesirable robot behavior is rated more positively after receiving an explanation. We thus aim to equip a social robot with the capability to automatically generate verbal explanations of its own behavior, by tracing its internal decision-making routes. The goal is to generate social robot behavior in a way that is generally interpretable, and therefore explainable on a socio-behavioral level increasing users' understanding of the robot's behavior. In this article, we present a social robot interaction architecture, designed to autonomously generate social behavior and self-explanations. We set out requirements for explainable behavior generation architectures and propose a socio-interactive framework for behavior explanations in social human-robot interactions that enables explaining and elaborating according to users' needs for explanation that emerge within an interaction. Consequently, we introduce an interactive explanation dialog flow concept that incorporates empirically validated explanation types. These concepts are realized within the interaction architecture of a social robot, and integrated with its dialog processing modules. We present the components of this interaction architecture and explain their integration to autonomously generate social behaviors as well as verbal self-explanations. Lastly, we report results from a qualitative evaluation of a working prototype in a laboratory setting, showing that (1) the robot is able to autonomously generate naturalistic social behavior, and (2) the robot is able to verbally self-explain its behavior to the user in line with users' requests. In recent years, the ability of intelligent systems to be understood by developers and users has been receiving increasing attention. Correspondingly, there is a rapidly growing body of work in the field of Explainable A.I. (XAI). Less work so far has been directed to this question in the field of robotics and human-robot interaction, although explainability is particularly important for social robots that are supposed to act autonomously in the vicinity of human users. Such robots are known to raise peculiar, often unrealistic attributions and expectations because users tend to anthropomorphize and ascribe intentionality to them  is hardly separable from the explainer (the robot) or delimitable from the interaction: the explanandum is being established within the interaction and evolves depending on which behavior is executed (and when) as well as how it is perceived by the explainee (the user). This, in turn, will generally depend on the user's prior experiences and expectations as well as general attitude toward social robots, all of which can change over the course of the interaction. Previous studies have shown that a robot's verbal explanations can increase the understandability and desirability of its behavior when rated by passive observers . Based on this we will derive general requirements for explainable social robot architectures. In Section 3, we propose a user-centered framework for autonomous, explainable behavior generation and lay out a dialogic model for responding to explanation requests in human-robot interaction. In Section 4, we present an implemented interaction architecture, focusing on how the proposed autonomous behavior and explanation generation processes are realized. Subsequently, Section 5 reports insights from a first evaluation study, before discussing and concluding with a wrap-up as well as an outlook on future work (Section 6).Several architectures have been developed for social robots focusing on different aspects of social interaction  as a means of successfully communicating a robot's intentions to pedestrians  has been shown to influence explanatory preferences on the basis of a person's characteristics such as age  principles and drive theory, our robot is equipped with internal needs that steer its behavioral choices . To that end, she interprets and evaluates the observed behavior based on her current mental model of the robot, which provides the basis for her expectations about the robot. The observed behavior may be interpretable resulting in the user's understanding and, possibly, acceptance of the behavior. In other cases, the observed behavior may not fit the user's current robot model, i.e., it is not fully interpretable and thus can result in an explanation request. Once such an explanation request is issued by the user, the robot interprets the user's request and generates a behavior explanation revealing information about the behavior or the reasons that led to it, as stored in a robot's self model. Upon receiving the robot's self-explanation, the user will update her mental robot model and re-evaluate the behavior based on the newly gained information, either resulting in a sufficient understanding of the behavior, or triggering further explanation requests. The robot must then be able to elaborate on its previous explanation, in a dialogic fashion that takes into account the user's current requests as well as the previous discourse. In this way, anchoring the robot's behavior explanations in the social interaction and enabling the user to evoke explanations via verbal inquiries allows them to actively co-determine when and what kind of explanation is sufficient. The underlying social interaction loop along with the different, involved communicative actions can most naturally and intuitively be implemented in a language-based dialog interaction.More specifically, we frame the interactive process of constructing an explanation as follows: A user continually when to provide what kind of explanation. That is, we differentiate between different kinds of user requests, explanation types and dialog contexts in which they can be employed. Following Madumal et al.  intentions or strategies the robot selected to pursue, (c) concrete actions executed by the robot. In addition, to enable more complex and elaborate explanations, these domains of intentional behavior can be causally linked in structured explanations, leading to (d) intention formation-based explanations (linking need and intention), and (e) action selection-based explanations (linking intention and action).The different types of explanations included in the dialog flow are inspired by the different “domains” found by Malle in studies on how humans explain intentional behavior Malle, , 1999:  modules for processing multimodal perceptual input for interpreting the active interaction context; (ii) modules involved directly in autonomous and needs-based socio-interactive behavior generation; (iii) modules involved in autonomous explanation generation; (iv) and modules needed for multimodal behavior realization. A graphical representation of the implemented interaction architecture is given in Perception component receives data from the different sensors attached to the embodiment. These sensor data are processed to interpret relevant information about the state of the internal and external environment of the robot, and make this information available to other components. The perception component includes separate modules for handling different types of sensor data. For example, the Interoception module deals with the physical state of the robot, such as the state of its joints and batteries. The Face Perception module receives image data from the robot camera, and analyzes it to detect and recognize a face. It builds on the dlibTM library ]. Afterwards, the communicative intent is inferred from the recognized verbal phrases by the Natural Language Understanding (NLU) component. With the help of an incremental information processing strategy, the verbal phrases and intent are updated incrementally, as new speech input arrives. In the current implementation, we use Google ASRDialog processing begins with analyzing the speech input from the user to recognize the spoken words .” For example, for the action greet_user, [action_name] is substituted with the sub-phrase “greeting you” and for drive_to_charging_station, it is substituted with “driving to the charging station.” An intention explanation inserts the name of the active strategy into the template “I wanted to [strategy_name].” For example, for the strategy StrategyInitiateContact, the sub-phrase is “establish contact with you.” The extended explanation inserts multiple phrases into a template. Precisely speaking, it repeats the last delivered explanation and extends it with causal information about the intention or the most relevant need. Extended explanations include “I am [action_phrase], because I wanted to [strategy_phrase].” and “I wanted to [strategy_phrase], because my need for [relevant_need_name] was [relevant_need_level].” The sub-phrases for substituting [relevant_need_name] are identical to the name of the need. The continuous-valued needs are discretized into three levels by applying two thresholds (0.3 and 0.7). Accordingly, the level of the need is termed as “high,” “medium,” or “existent.” All verbal explanation phrases are accompanied by a hand gesture of the robot.The Dialog Manager and, in this way, enables explaining and elaborating according to users' needs for explanation evolving in the interaction. The interplay of the input and output processing modules, the socio-interactive behavior generation modules, and the explanation generation modules, enables users to request and receive incrementally updated self-explanations of the robot, at their preferred time or level of detail during an ongoing dialog (Requirements 4 & 5).In sum, the explanation dialog flow model proposed in Section 3 is implemented by the issue structure of the Strategy or utterances requested by the Dialog Manager. The Behavior Controller instantiates any requested behavior and keeps track of its execution status. In addition, it coordinates the allocation of hardware resources between individual behavior instances and acts as a proxy for communication between individual behaviors and other components of the architecture. Multimodal communicative behaviors requested by the Dialog Manager, are dialog acts specified in the Behavior Markup Language (BML)  that may be requested by the active Strategy or by the user directly, are realized using Locomotion and Gesture Engines.This component is responsible for coordinating and managing the execution of communicative and non-communicative robot behaviors, which are either the elementary actions of the active via dedicated behavior engines. The Speech Engine handles the commands for verbal output, the Gesture Engine handles body animations and individual joint movements, and the Locomotion Engine handles the rotation and translation of the whole robot in space. The engines provide the execution status of commands in individual status messages, which are then distributed to higher-level components via the Behavior Controller. The implementation of these engines differs depending on the used robot. In our use case, to communicate with the robot PepperSpeech Engine can use Pepper's TTS, the Gesture Engine can execute pre-defined animations and joint motion trajectories safely, and the Locomotion Engine can drive the robot at safe velocities. We also use the Basic Awareness ability of NAOqi to realize the gaze behavior and enable the robot to react to people and movements in the environment (sound and touch stimuli are currently ignored). Although the Behavior Engines and the Behavior Controller are inspectable due to their declarative nature (Requirement 1), the models used for realizing the behaviors on the hardware might not always be available for inspection, due to the proprietary nature of those libraries.The transformation of individual behavior elements into embodiment-specific commands is done via the ROS middleware. The communication between all other components, modules, and models uses the Python 3 implementation of an incremental communication framework , all other components are independent of the physical embodiment and can be easily ported to other social robot or virtual agent platforms.In the current implementation of the architecture, communication between sensors, actuators and other hardware components takes place To test the proposed framework for social robots' behavior explanations, as well as its implementation in the presented architecture, we have carried out a first evaluation in live human-robot interactions in a lab setting. More specifically, a qualitative evaluation was designed to investigate if the first working prototype fulfills both, thevia evaluation of the robot's ability to (1) sense and process multimodal information about the social context, (2) generate naturalistic and coherent multimodal behavior, and (3) fluently adapt its behavior to dynamic changes in the social interaction contextarchitectural and functional requirements, via evaluation of the robot's ability to deliver explanations in parallel with generating behavior (Requirement 4) and according to the needs of the user (Requirements 3 & 5), indirectly validating adherence to Requirements 1 & 2.explainability requirements, The evaluation included an acquaintance interaction between a human user and the social robot “Viva”  situated in a laboratory living room setup. Throughout the interaction, the robot was entirely controlled by the above-described behavior generation architecture. Ethics approval was obtained from the ethics committee at Bielefeld University and COVID-19-related hygiene and safety precautions were taken.The procedure of the study is depicted in via dialog. The following strategies were available to the robot: StrategyIdle, StrategyInitiateContact, StrategyGreeting, StrategyAcquaintWithUser, StrategyLoosenUp, StrategyChargeBattery. As the robot's perception did not yet account for recognition of a user's presence in the room, this memory value was manually set to true, once the experimenter had left the lab and the participant was alone in the room with the robot, setting the preconditon for the StrategyInitiateContact. Due to the restricted interaction time during which the robot was fully operable, the robot's energy status was not updated based on its actual battery status. Therefore, in order to evoke the selection of this strategy, experimenters decided to manually send an event that increased the robot's need for energy after some time. An example video of the interaction (with the researcher in the participant role) can be accessed onlineThe interaction was not scripted but unfolded through the robot interacting autonomously with the participants. That is, the robot was able to select strategies, execute the corresponding behaviors, and communicate with the users via quantitative items, as well as open questions. Robot's behavior was rated by participants with regard to intentionality, understandability, surprisingness, and desirability. Regarding the robot, ratings of likeability  were between 23 and 34 years old . 5 participants were students and the majority of the participants had not interacted with a social robot prior to this interaction (N = 8). Quantitative data was analyzed using JASPIn total, 12 participants took part in the interaction with the robot. One interaction had to be canceled due to a system failure and was thus excluded from the analysis. This led to a total of sensing and processing multimodal information about the social context and (3) fluently adapt its behavior to dynamic changes in the social interaction context, the data stored in the interaction protocol was analyzed: The robot selected adequate behavioral strategies based on the current interaction mode. To start off the interaction manually, the StrategyIdle was set to success and the memory event (agent_detected) was sent to the robot once a participant was present and after the experimenter had left the room. The robot was able to process this information, leading to a change in its interaction mode from Alone to Co-Presence and triggering the robot to approach the user. In all interactions, the robot was idling (StrategyIdle) while Alone and once detecting the user in the room, switched to interaction mode Co-presence and reliably decided for StrategyInitiateContact and StrategyGreeting. Subsequently, if the robot was able to detect the user's face, it switched to the interaction mode Interaction and selected the now available StrategyAcquaintWithUser. If not, the robot remained in the interaction mode Co-Presence and started with StrategyLoosenUp, followed by re-starting the gaze behavior and selecting the StrategyAcquaintWithUser, if the users' face was in view now and the interaction mode thus changed to Interaction. Upon reception of the energy_drop event, the robot's need for energy increased, leading to the selection of the StrategyChargeBattery. The robot was further able to sense and process verbal input, as well as visual input about the user's face, enabling it to successfully gather user information in the acquaintance dialog during every interaction.In order to analyze fulfillment of the architectural and functional requirements (1) naturalistic and coherent multimodal behavior generation is best given from the users' feedback: Due to the small amount of participants and a high variability between the interactions that unfolded autonomously, results from the analysis of the questionnaire data should be regarded with care, indicating at best a general direction. Mean ratings of the behavior and robot ratings were analyzed. Tests against the scale mean of four reveal that the robot's behavior was generally evaluated as intentional , surprising , and desirable . Ratings with regard to the robot's behaviors' understandability were not statistically different from 4. Further, the robot was perceived as likable , trustworthy  and intelligent . As a response to the question which moments participants perceived as natural during the interaction, seven participants referred to the conversation flow and four explicitly named the nonverbal behavior, suggesting that the multimodal behavior generation was overall perceived as naturalistic. When asked about unnatural behavior, five participants referred to the verbal interaction being not entirely intuitive, referring to difficulties with regard to turn-taking and seven participants referred to the robot's stretching or moving away without previous notice as irritating.An assessment of whether the implemented architecture enabled (2) via assessment of Requirements 3–5. The interaction protocols reveal that 7 what-, 8 why-, and 1 elaboration- request were correctly answered. This assesses the robot's capability to understand verbal requests from the user, to dynamically select the correct explanation type, and to retrieve the information needed for an explanation from within the architecture (inspectability). The robot was thus, in general, able to deliver user-centered explanations (Requirement 5), further validating fulfillment of Requirements 1–4.The explainability requirements that refer to individual architecture components (Requirement 1) and their communication interfaces (Requirement 2) could not be explicitly tested with the interaction, but are indirectly validated M = 5.27, t(10) = 3.55, p = 0.003, d = 4.43], the explanations were not rated as particularly justifying the robot's behaviors (M = 4.09) or helping participants understand why the robot behaved as it did (M = 4.0).Strikingly, out of the 16 correctly identified requests, nine were uttered while no action (5) or strategy (4) was currently active, leading to participants receiving a correct, but only sparsely helpful answer . While this can be attributed to the explanation info being updated incrementally and in parallel to the behavior generation, and attests fulfillment of Requirement 4, the outcome is not desirable and should be incorporated in the explanation dialog flow model. For instance, in this specific case, one could switch to past episodes to explain the last active action/strategy if no active strategy is currently available. This observation is in line with the survey data: while the robot's explanations were rated as understandable [mean statistically significantly higher than scale mean of 4: via an action command sent from the dialog manager and thus not represented in the robot's self-model for explanation generation. While this is a shortcoming of the current implementation, it simultaneously supports the underlying claim that the robot's behavior needs to be explainable at any time. Notwithstanding, the seven explanations uttered while a strategy or behavior was currently being executed show that the robot was able to successfully verbalize the current behavior with the adequate explanation strategy if deliberately chosen via its internal decision process.Interestingly, further inspection of the protocols reveals that these explanation requests were often uttered in relation to the robot's action of driving away from the user. This behavior was reactive, being directly triggered StrategyAcquaintWithUser) and sequences where the robot did not have the dialog initiative. These difficulties with the dialog management, the relatively large number of “no operation” explanations and mis-recognized requests may also offer an explanation for the overall relatively small number of elaboration requests.In addition, while the above examples demonstrate that the robot is in principle able to provide behavioral self-explanations, a large number of verbal explanation requests by the user were not correctly identified  and were thus not answered correctly. Improvement of the processing of language-based explanation request thus proves essential: In addition to the expansion of NLU training data, it seems indispensable to consider the socio-interactive context when processing the user's verbal input. This goes in hand with participants' broad consensus regarding suggestions for improvement: 9 of 11 participants mentioned insecurities with respect to the verbal interaction with the robot. Participants reported issues such as non-intuitive turn-taking due to the robot's response times and feeling unsure about conversational roles especially mentioning switches between system-initiated dialog sequences explanations of their own needs-based behavior, at any time during a running interaction. These explanations are socio-interactively constructed to effectively fit the current information need of a human user. To that end we have combined insights from research on human-robot interaction architectures as well as explanation generation, in order to derive concrete requirements for explainable behavior generation architectures of social robots. We have formulated a socio-interactive framework for self-explanation generation, incorporating empirically validated explanation strategies and closely embedded in the behavior generation architecture. Further, we have described an implementation of this architecture realizing this concept and addressing the requirements.A first evaluation has proven that the proposed architecture presents a useful foundation to meet the challenges of explainable autonomous behavior generation. As previously stated, the results of this initial experiment were gathered with a confined user group interacting with the robot in a first basic greeting use case. Therefore, applicability to divergent target groups and transferability to more complex use cases need to be assessed in future experiments. Nevertheless, our results suggest that the socio-interactive approach is, first, conceptually and technically feasible and, second, accepted and appreciated by real users: participants ask what the robot is doing and why it is doing so, and the robot is able to introspect its reasoning and to present it in ways that were previously shown to enhance user understanding and acceptance of the robot's behavior—necessary pre-requisites to support user-centered explanation delivery. At the same time, our evaluation has provided valuable insights on how to improve the technical realization: while the robot is able to correctly address users' explanation requests in specific situations , some explanation requests were not adequately answered by the robot . This showcases the necessity to increase the level of socio-interactivity by linking perception with explanation generation.Accordingly, we identify the following starting points to extend the current implementation: in order to improve the processing of users' explanation requests, the NLU needs to be improved by extending its underlying training data, and also by grounding it more strongly in the interaction context. Similarly, the robot's explanation capabilities need to be extended such that the robot, in addition to its deliberately chosen behaviors, is also able to introspect its reactive nonverbal and verbal behaviors that were executed as a direct response to events in the environment. This can be enabled by extending the robot's self model and episodic memory to link all behaviors to the  events in their causal history. This leads to another important extension: giving memory-based explanations for past behavior, for example if the robot is currently doing nothing, but executed an action shortly before the user's explanation request. For this, the recently closed interaction episode could be checked. Taking this one step further, explanations could entail causal links that express events that led to changes of robot needs, which in turn led to selection of a certain strategy . The proposed architecture has proven to be a useful approach to explainable social behavior generation. At the same time the evaluation has shown that, in order to exploit the full potential of such an integrated approach, the socio-interactive capabilities of robots have to be extended further, enabling more robust perception of the users' needs for explanation and thus enabling social robots to optimally deploy self-explanations, increasing the understandability and desirability of robot behavior for their human users.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Ethik-Kommission der Universität Bielefeld (EUB). The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.SS and SK conceived the explanation dialog framework. TH, SK, and SS devised the explainable behavior architecture. TH, FS, SS, and JK implemented and tested the architecture, wrote sections of a draft manuscript, and designed the figures. SS, FS, JK, and SK designed the study. SS administered and prepared the study and performed statistical analysis of the questionnaire data. FS, JK, and SS conducted the study and analyzed the qualitative interaction data. SS, TH, and SK revised and finalised the manuscript after review. All authors contributed to the manuscript, read and approved it for submission.This research was supported by the German Federal Ministry of Education and Research (BMBF) in the project VIVA (FKZ 16SV7959). This research was also partially supported by DFG in the SFB/TRR 318 Constructing Explainability. We acknowledge support for the publication costs by the Open Access Publication Fund of Bielefeld University and the Deutsche Forschungsgemeinschaft (DFG).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The fifth-generation (5G) wireless network is visualized to offer many types of services with low latency requirements in Internet of Things (IoT) networks. However, the computational capabilities of IoT nodes are not enough to process complex tasks in real time. To solve this problem, multi-access edge computing (MEC) has emerged as an effective solution that will allow IoT nodes to completely or partially offload their computational tasks to MEC servers. However, the large communication delay at a low transmission rate for nodes far from the access point (AP) makes this offloading less meaningful. This paper studies joint multi-task partial offloading from multiple IoT nodes to a common MEC server collocated with an AP, and it uses relay selection to help nodes far from the AP. The computation time of all tasks is minimized by adaptive task division and resource allocation (bandwidth and computation resource), and it is solved with an evolutionary algorithm. The simulation results confirm that the proposed method with both relay selection and adaptive bandwidth allocation outperforms the methods with neither or only one function. Recent developments in wireless communications and sensing techniques have promoted the Internet of Things (IoTs). Initially, IoT nodes were used to realize simple sensing tasks, such as measuring temperature or moisture, and most IoT nodes have limited resources for computation, storage, and battery life.The emergence of AI techniques enables more advanced tasks, such as surveillance and intrusion detection, which heavily rely on the deep learning technique to improve their accuracy and require more complex processing at IoT nodes . AlthougTo overcome the above problem, in the last few decades, mobile cloud computing (MCC) has been suggested to assist IoT nodes with low computational capabilities by exploiting the abundant processing power and storage in the cloud. Specifically, IoT nodes collect and send data to cloud servers for processing, and the control decision based on the processing result is sent back to actuators collocated with the sensors. This helps to reduce the processing delay, but the long distance between IoT nodes and the cloud servers may bring vast communication latency, especially when the sensing data are multimodal with a large size, which makes it hard to meet the criteria of delay-sensitive applications ,4.A new technology, named multi-access edge computing (MEC), has been proposed to meet the strict delay requirement. An MEC server is deployed at the cloud edge near IoT nodes. With more computation resources and storage than IoT nodes, an MEC server can help IoT nodes to process computation-intensive tasks, while its short distance to IoT nodes also helps to suppress the communication delay ,6,7.The computation task offloading to MEC servers has attracted much attention in recent years. It is generally divided into two types: binary offloading and partial offloading . In binaIoT nodes usually are connected with MEC servers by wireless communications. The wireless channel is susceptible to path loss and channel fading, which may greatly degrade the transmission rate. When a task has a very large data set, but the transmission rate between the IoT node and the MEC server is low, the communication itself takes a great deal of time, and the effect of partial offloading will be degraded. Relaying was exploited in  to allevThis paper studies joint multi-task partial offloading from multiple IoT nodes to a common MEC server collocated with an AP, aiming to address the heterogeneity in transmission rates. To this end, an algorithm named Partial Offloading with Relay and Adaptive Bandwidth Allocation (PORAB) is proposed to minimize the execution time of all tasks. PORAB is an evolutionary algorithm. It initiates a population of candidates. In each generation, it drops the lowest part of the population and generates new candidates using the characteristics of highly fit candidates.We investigate the heterogeneity in transmission rates and suggest using a relay to help nodes far from the AP and combine it with adaptive bandwidth allocation to achieve fine-grained resource allocation.We propose an evolutionary algorithm for joint optimization of radio resources (bandwidth) and computation resources. This not only improves the performance of nodes using relays but also reduces the delay of other nodes.Our contribution is twofold:The proposed method is evaluated with MATLAB simulations, and the impacts of the number of tasks, size, and the volume of data on the task execution time are analyzed. The simulation results confirm that the proposed method with both relay selection and adaptive bandwidth allocation outperforms methods without the functions or with only one function.The remainder of the paper is divided into five sections. Related works are reviewed in Partial offloading is especially suitable for applications with low latency requirements. Some work has been performed in this field. In , Wang etIn , consideIn recent years, intelligent offloading has attracted much attention. In , Qu et aThis paper also adopts the policy of partial offloading. Compared with previous work in this field, this paper considers the potential poor transmission rate and time-variant data size in each task. Accordingly, relay selection is used to help nodes far from the AP, and adaptive bandwidth allocation is exploited to address both the heterogeneity in transmission rates and the variation in data volumes of tasks. We notice that relay was exploited in , where aN IoT nodes. Nodes are randomly distributed in the coverage of the AP. They have low computational capabilities and cannot process power-hungry and computation-intensive tasks in real time. Therefore, an MEC system is deployed, collocated with the AP to deliver computational services to the nodes on demand.In this paper, we consider a wireless cell with an AP and n has a data size n is n is B, and the percentage of bandwidth for task n is n as We assume each node has only one task. The task at node When a node offloads its computation task to the MEC server, the delay consists of three parts: (1) time for transmitting data to the MEC server via the AP,  (2) time for processing the data at the MEC server, and (3) time for transmitting the result from the MEC server back to the node. Here, it is assumed that the size of the result is negligibly small. The time in (1) depends on the transmission rate, which further depends on the distance between the nodes and the AP. As shown in n to the AP isn, n and the AP, n, and B represents the total bandwidth of the system.According to the Shannon theory, the rate for direct transmission from node n transmits its task data via a relay node j, it shares its bandwidth with the relay node, and they transmit in different periods. The whole transmission involves two rates: n to relay node j in the first period and j to the AP in the second period, as shown in When node n and the AP, using relay j, isThe period length is inversely proportional to the transmission rate, and the ratio between the first and second period lengths is j that maximizes the rateThe relay selection problem is finding a relay node n to the AP isIf n locally processes a part .Mathematically, we define our joint optimization problem as follows:The objective of Equation  is to miEquation  states tEquation  ensures Equation  indicateAs can be seen in Equation , our objn reaches the minimum. Then, we can calculate the value of In our optimization problem, time delay consists of the local computation time Equation  and the Equation , with boAfter some mathematical calculations, we find the optimal value of task division As can be seen in Equation , the tasThen, the non-convex optimization problem in Equation  can be rn in the OFDMA-based network. In [The objective in Equation  is a nonquations  and . During each iteration, the algorithm computes the maximum in Equation  as the fv, respectively. If its window size For each dimension s solutions required to sort the population are selected, and the complexity is L. Accordingly, the overall computation cost is In each iteration of Algorithm 1, the computation mainly takes place in three places. First, there is the computation of the fitness value Algorithm 1: Partial Offloading with Relay and Adaptive Bandwidth Allocation (PORAB)N, where T. We observed that as the number of iterations increased, the algorithm gradually converged to an optimal solution. The converged result of the algorithm increased with the number of nodes N because more nodes led to more computational requirements at the MEC server.The convergence graph of the proposed algorithm is shown in In this section, the performance of the proposed method is evaluated via simulation in the MATLAB environment.N IoT nodes were randomly distributed in the coverage of an AP, and the channel between a node and the AP was constant within the transmission time.The simulations were performed by using the parameters shown in The proposed PORAB method has both relay selection and adaptive bandwidth allocation functions. It was compared with other methods, namely Partial Offloading with neither function (POBase) , PartialIn the evaluation, we used the transmission rate and task computation time as the evaluation metrics. The latter was the maximal execution time of all tasks, each of which was the maximum of local processing time When each task was allocated equal bandwidth, using a relay did not help much, and the task computation time was almost the same for POBase and POR. When adaptive bandwidth was used, both PORAB and POAB achieved better performance than POBase and POR. In addition, the effect of the relay became obvious. PORAB outperformed POAB, and the improvement in task execution time increased with the number of tasks.In our model, we optimally allocated the bandwidth, and we can see in The above results indicate that both relays and adaptive bandwidth allocation helped to reduce the task computation time. Adaptive bandwidth allocation fine-tuned the transmission rates and showed a very obvious effect, regardless of the use of relays. In comparison, the effect of the relay was larger when used together with adaptive bandwidth allocation.In this paper, to solve the potential transmission delay caused by the low transmission rates of nodes far from the AP in multi-task partial offloading, we have suggested using relay selection and adaptive bandwidth allocation. We minimized the overall task computation time, jointly considering relay selection, bandwidth allocation for tasks, task division between nodes and the MEC server, and resource allocation for tasks at the MEC server. PORAB, as an evolutionary algorithm, was proposed to iteratively solve the problem. The simulation results show that the proposed method effectively reduced the task computation time compared with other benchmark methods.


Education is a part of human basic needs, and one way to provide this need is through schools. Therefore, schools should be accessible to all members of society without exception. Jakarta, the capital of Indonesia, has 3899 primary and secondary schools to provide for over 1 million school-age children as of 2020. This research, however, will not dwell on the issue of capacity based on the number of demands and available schools, but rather the accessibility and fairness. Two methods: radius and road network, are used in this research with the addition of spatial analysis to measure the students’ walking accessibility level relating to the school zoning system regulations in Indonesia. The analysis result highlighted the variations in accuracy between the two methods, the coverage area between formal and informal settlements, and the spatial context of the accessibility. The result provides a comprehensive spatial analysis to support policymakers in future planning works. Spatial distribution; School; GIS; Accessibility; Equity. Elementary-level education, in particular, is compulsory and should be free of all costs . EducatiThe availability aspect may have been responded to by building schools and providing supporting facilities such as books, equipment, and lecturers. However, the aspect of accessibility implies questioning whether those physical buildings, facilities, and human resources are distributed to those in the manner intended to ensure their well-being . Yet resFrom the development perspective, typically, urban development begins in the center and progresses to the outskirts. This pattern is followed by the development of public facilities such as hospitals, parks, and schools. Therefore, when an urban area has reached its peak development, many public facilities are centralized in the heart of the city . MeanwhiThe situation is similar in Indonesia, a Southeast Asian developing country. The 1945 Indonesian Constitution stated that education should be accessible to all and the government then further established a nine-year compulsory basic education policy in 1989 . The polAside from the compulsory education policy, the government seems to be working on the affordability aspect. For example, a stimulus program called Program Indonesia Pintar or Smart Indonesia Program (PIP) was launched by the President to assist children aged 6 to young adults aged 21 to finish their compulsory education. The program is prioritized for children from low-income families, orphans, natural disaster victims, and children with disabilities, according to the Ministry of Education and Culture Regulation No. 10/2020 . For genIt is safe to say that the government's efforts have covered the accessibility aspect from legal, regulatory, and financial perspectives. This prompted a question: what about the accessibility aspect from a spatial perspective? Related to this is the Indonesian school zoning system, where student acceptance to public schools is based on the proximity of the facility to the student's domicile. This zoning system is regulated at the local government level and described in detail. In Jakarta, for example, school zoning is regulated (and updated) through Governors' decree No. 440-year 2022. Here the information is as detailed as which neighbourhood in which sub-district is included in the school's service area and their prioritization level. Even though the description is very detailed, the basis is unclear. The ministry omitted the distance measurement from the zoning policy at the national level, considering the large variety of geographical or terrain conditions spread across the archipelago, and only emphasized the nearness of the education facility to the student's domicile as a requirement .Distance to a public facility is a valid parameter to measure service coverage areas and increase the service rate. The Indonesian school zoning system, on the other hand, had the opposite effect: while it was initially around 70–90% in 2018, it dropped to 50–70% in 2019 , 2019. TThe issue might be caused by the intention behind the school zoning system, which was mainly to ensure equitable service across the country. This system was meant to, amongst other things, reduce schools' exclusivity and discrimination, better distribute teaching materials and resources, and ensure the financial assistance programs are delivered to the targeted awardee . HoweverThe reason why underage students drive motorcycles to school is that most parents permit them to do so for several reasons: security, convenience, and cost. The security issue is related to criminal acts that take place in public vehicles; convenience is related to route picking, and the cost is related to public transportation fees. Students riding motorcycles can cut travel costs and time commuting to schools while at the same time avoiding the crime risk of public vehicles. In addition, many motorcycle dealers offer low instalment pay with zero down payment , which, The government's attempt at a zoning system should be adequate to answer those worries. By studying in nearby schools, students will be able to travel by foot, thus reducing their exposure to public vehicle crime risk and traffic accident risk while lowering the cost and time spent travelling to and from school on a daily basis. Yet still, traffic accidents related to or caused by underaged students are still high, even after three years of the zoning system's establishment. This indicates two causes: first, the zoning was poorly calculated such that the travel distance is over the limit of comfort for travel by foot; second, the route is spatially inconvenient, thus unable to accommodate pedestrians.The situation in Indonesia indicates that the provision of facilities for basic human rights, such as education, is not enough to be available. It demands good accessibility because the policy regarding availability would be useless if, at the end of the day, it was not able to provide equal service to those as intended . Equal aThe research case study uses Jakarta because of the distinct wealth gap evident in the quality of formal and informal settlements. This is essential to highlight differences in planning quality. The city also has its share of slum areas, and a large portion of its demographic is school-aged children . In accoThe study began with calculating service area using radius and road network methods based on walking distance, identifying the most facilitated formal and informal settlements to compare, and analyzing the spatial qualities of these settlements. The situation regarding the existing public service facilities is essential to future development planning. Therefore, it is necessary to consider both spatial aspects, such as location and access, and non-spatial aspects, such as user demographics, in the planning process . The ass2Research on access equitability has been done towards healthcare e.g.: , schoolsEquitable public facilities from users’ perspective were measured by availability within a specific area , proximiSpatial accessibility is often assessed by considering the amount of cumulative opportunity—i.e., the number of relevant facilities or destinations—reachable from a particular point within a certain distance or travel time . SpatialNon-spatial accessibility emphasizes non-geographic barriers such as social class, income, ethnicity, age, and disabilities  which arMany empirical studies have assessed the accessibility of various facilities for different demographic groups; some have looked simply for all facilities within a given radius , while oNicholls, Oh and Jeong, and Salarvandian adopted both the radius and network analysis methods to study the accessibility and equity of public service distribution . The sim33.1This study used informal settlements data provided by City Without Slum or Kota Tanpa Kumuh abbreviated as KOTAKU and Statistics Indonesia . Slums oA slum area is also identified from its population density of more than 200 people per square kilometer . The typ3.2The number of schools and students including private and public schools based on the educational levels from elementary to high school in Jakarta are listed in 4This research primarily utilized GIS information available from a government, NGO, and open-source websites as the object of analysis. Field observation was not possible because the research was conducted during the pandemic. Therefore, the information regarding physical condition of accesses was retrieved from Google applications such as Map and Street View. A detailed list of information and sources is available in the following The GIS data were then analysed, together with distance measurements, to provide information on the educational facilities' service area coverage. Accessibility is defined as the distance that individuals had to travel to reach a particular school from their residence . TherefoAfter defining the educational facilities' area coverage, the study continues with analysing accessibility and social equality. From the literature review, the research compiled two dimensions and their respective attributes to measure accessibility. A scoring index was created to ease the calculation, as presented in 55.1This section provides the schools' service areas and the area coverage comparison between formal and informal settlements in the five districts of Jakarta. The measurement of distance is adjusted according to the level of the educational facility, as presented in The service area coverage was also used to calculate the ratio of facilitated areas at formal (CRf) and informal settlements (CRi) by tracing them onto the settlement map provided by KOTAKU. The coverage ratio of schools at formal and informal settlements is similarly calculated per district as illustrated in The service area coverage data was calculated using the radius and road network method. The radius method relies on the recommended traveling distance measured outward from the location of the facility with no regard to access. Meanwhile, the road network method applied the recommended traveling distance from the facility's location outward based on available accesses. The radius network analysis showed the educational facilities coverage ratio in Jakarta, except Kepulauan Seribu district, as low. However, The coverage analysis result was placed above the KOTAKU's settlement map to investigate the coverage ratio per formal and informal settlements. The result is presented in This pattern is repeated in the South Jakarta district, only with a slight variation. The elementary and senior high coverage ratio based on radius method and junior high based on road network method are higher at informal settlements in the district. Meanwhile, the contrasting results are presented in other categories.The coverage ratio analyses have shown the broadness of schools’ service areas in their respective district. Despite the road network method providing a more realistic result, as in really put access into consideration instead of merely drawing a radial image of distance, further analysis on the spatial aspect is still required to assess the accessibility. The spatial analysis concerns seven attributes of two dimensions: safety and comfort listed in Previously mentioned that the research would commence a spatial analysis upon well-facilitated formal and informal areas. The coverage ratio analyses pointed to Central Jakarta as the best-facilitated district with its three formal settlements: Gunung Sahari, Pasar Baru, and Cempaka Putih Timur, and three informal settlements: Karet Tengsin, Bendungan Hilir, and Menteng. The result is available in the following section.5.2The spatial analysis began with measuring the section drawing of accesses to schools in the area, which are made based on authentic images from Google Street View. Junior and senior high schools in Central Jakarta are primarily located in the north and eastern parts of the district alongside most of the residential areas. The rest of the district mainly consist of commercial, business, and government buildings. Pasar Baru in the north is the example of a well-facilitated formal settlement of this district. The pedestrian access to a high school in Pasar Baru village is shown in Another well-facilitated formal settlement in Central Jakarta is Cempaka Putih Timur. 5.3Following the spatial analysis of accesses in well-facilitated formal settlements is the analysis of informal settlements located in the Central Jakarta district. The informal settlements are part of Karet Tengsin, Bendungan Hilir, and Menteng administrative villages. The three informal settlements are located at the edge of the Ciliwung River. The accesses in these areas face similar problems of either the sidewalks are absent or discontinued. Sidewalks are available only on the major road, yet they are only one meter wide or less and occupied with various obstacles. From the index scoring, the informal settlement in Bendungan Hilir scored the highest at three points. This informal settlement is facilitated with sidewalks as it is located at a major road, albeit the many obstructions. Meanwhile, the informal settlements in Menteng and Karet Tengsin villages scored minus six and minus seven, respectively. The spatial analyses produced a similar result between formal and informal settlements. Despite the different road measurements, both show that the settlements located at major roads score better because the sidewalks are present at the very minimum.6The implication of schools' acceptance zoning system evoked protests from students' parents, particularly in Jakarta . The schThere are several ways to improve students’ access on foot to school, especially in informal settlements. The first way is by improving the quality of planning and road construction by developing the design regulations for road construction around schools, considering the aspect of safety and convenient access to school. With this, parents will not worry about the safety of their children when walking to school and will not be burdened with the cost of transporting their children to school. In addition, this study has a limitation that only evaluates the distance based on walking accessibility, so further research related to spatial justice based on commuting distance also needs to be analysed in the future. This will increase recommendations regarding how to build roads around schools where students have long commuting distances. In addition, with this research, the route of the free school bus facility can be improved so that it can reach all students, whether from formal settlement or informal settlement.In addition, this research was carried out during the Covid-19 pandemic, so there was a policy change from the Ministry of Education regarding learning methods from school to students' homes or online learning . Althoug7The radius and road network analysis of education facilities' accessibility in Jakarta produced contrasting results. Generally, the radius method provides a more favorable 63–82% coverage ratio for all education levels, while the road network method had 41–62%. There is approximately a twenty percent rate difference in the results. The highest coverage area is located in Central Jakarta, albeit the least number of schools. The district is equipped with well-connected roads, therefore, further increasing its accessibility. However, the comparison result of spatial analysis of well-facilitated formal and informal settlements in Central Jakarta shows that formal settlements still scored better than the informal despite being located in the same district.These findings indicate that this city's development is more well-thought in the center than in the peripheries. Unfortunately, even with such well-thought planning, the spatial treatment disparity between formal and informal settlements is still very much noticeable. The significant result gap between the two methods also suggests that the radius method is unsuitable for planning as it does not comprise the actual on-field data. Furthermore, the road network method should also involve spatial analysis when assessing accessibility. The overall result indicates that the education facility in Jakarta city is not well distributed and far from being accessible in terms of distance and spatial quality.The strength of this study is that this study shows a simple spatial analysis but produces a complex and crucial analysis related to spatial justice in the distribution of schools in Jakarta. Thus, this study can assist planners in achieving the ideal spatial justice of education facilities in the future. Planners and related stakeholders can utilize directly simple spatial methods such as the road network analysis to determine future development targets and overcome the social gap between society, particularly for educational facilities. However, the authors understand that in this study, some shortcomings or limitations can be developed in the future. This study limits the input parameter, focusing on the walking accessibility of students. Future research can improve the spatial analysis by expanding the analysis such as the travel accessibility with various of vehicles, method such as kernel density techniques, autoregressive models, census geographies, or travel time analysis, and creating comprehensive city data to assist urban development planning further. Finally, the city needs to achieve spatial justice to support the government in providing education facilities accessible to all as stipulated in the constitution.Ahmad Aki Muhaimin: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.Ahmad Gamal: Conceived and designed the experiments; Contributed reagents, materials, analysis tools or data; Wrote the paper.Michelle A. S. Setianto: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Wrote the paper.Widya Laksmi Larasati: Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.10.13039/501100014823Direktorat Riset dan Pengabdian Masyarakat Universitas, Indonesia [Grant # NKB-1750/UN2. RST/HKP.05.00/2020].Mr. Ahmad Gamal was supported by Data will be made available on request.The authors declare no conflict of interest.No additional information is available for this paper.