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Zero-trust security is essential to enterprises that are converging operational technology (OT) with IT infrastructure. New research from Enterprise Management Associates (EMA) identified how this convergence influences zero-trust strategy and implementation.
Companies have historically secured OT systems – which include physical security controls, HVAC systems, industrial control systems like factory automation equipment, and medical scanning equipment – by air-gapping them. Physical isolation from IT networks protects OT systems against threats, but it also prevents companies from driving more automation, efficiency, and intelligence into business operations.
Companies want flexibility in how end users and business applications access and interact with OT systems. For instance, air-gapped medical imaging equipment in a hospital requires a doctor to interact physically with the equipment to access their patient’s imagery. IT/OT convergence allows those doctors to access medical imagery from anywhere.
Enterprises also want to extract data from OT systems, which requires network connectivity. For example, manufacturers can pull real-time data from their assembly lines so that specialized analytics applications can identify opportunities for efficiency and predict disruptions to production.
While converging OT onto IT networks can drive innovation, it exposes OT systems to the threats that proliferate the digital world. Companies often need new security solutions to protect OT. EMA’s latest research report, “Zero Trust Networking: How Network Teams Support Cybersecurity,” revealed that IT/OT convergence drives 38% of enterprise zero-trust security strategies.
Zero-trust security is the application of granular authentication, authorization, and segmentation policies and controls to ensure least privilege access to networks. Zero trust often involves continuous verification of authorized access by analyzing behavior and challenging devices and users to reauthenticate themselves. Zero trust is especially valuable to OT convergence because it can authenticate access based on factors other than user identity, and it can apply granular network segmentation to restrict what kinds of communications OT devices can have on an IT network.
EMA’s new zero-trust research, based on a survey of 270 IT professionals, found that IT/OT convergence correlates with a different approach to this security model. For instance, the top two guiding principles of zero-trust initiatives in general are (1) preventing unauthorized access and (2) preventing zero trust from negatively impacting network performance and user experience.
With OT-focused zero trust, enterprises are less concerned about network performance impacts. Instead, they place a greater premium on management simplicity. OT convergence adds network complexity by expanding the number of devices that need to connect and increasing the amount of segmentation needed to isolate those devices. Zero-trust architectures that are built for management simplicity can mitigate these issues.
IT/OT convergence leads enterprises to set different priorities for zero-trust solution requirements. When modernizing secure remote access solutions for zero trust, OT-focused companies have a stronger need for granular policy management capabilities. These companies are more likely to have a secure remote access solution that can cut off network access in response to anomalous behavior or changes in the state of a device.
When implementing zero-trust network segmentation, OT-focused companies are more likely to seek a solution with dynamic and adaptive segmentation controls. These companies also perceive a greater need for a network observability tool that can support zero trust. Typically, they want an observability tool that can facilitate access policy design.
EMA’s research asked respondents to identify various challenges to their zero-trust projects. Our analysis found that OT-driven projects had a few unique issues. First, we found that these companies are more likely to struggle with adapting legacy secure remote access technologies like VPNs to zero-trust requirements.
Moreover, OT-focused companies were more likely to perceive an overall lack of effective zero-trust products on the market. Apparently, most zero-trust solution providers are focused on traditional IT use cases, rather than OT. These companies were also more likely to tell EMA that their network observability tools are failing to support zero trust, suggesting that they need new monitoring tools that can extend their visibility into OT systems.
To learn more about EMA’s new zero-trust networking research, check out this free on-demand webinar, which highlights our key findings.
Shamus McGillicuddy is the research director for the network management practice at Enterprise Management Associates. He has been covering the networking industry for more than 12 years as an analyst and journalist. Prior to joining EMA, Shamus was the news director for TechTarget’s networking publications. He led the news team’s coverage of all networking topics, from the infrastructure layer to the management layer. He has published hundreds of articles about the technology and competitive positioning of networking products and vendors. He was a founding editor of TechTarget’s website SearchSDN.com, a leading resource for technical information and news on the software-defined networking industry.
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Humanities and Social Sciences Communications volume 11, Article number: 716 (2024) Cite this article
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The purpose of this study was to analyze text-based comprehension questions asked by fourth-grade teachers in Turkish (L1) lessons in emergency distance education that started during the COVID-19 pandemic and to compare the results with those of other studies conducted in face-to-face educational settings. The text-based comprehension questions were evaluated in terms of different text types (narrative and expository) according to the distribution of the questions in the reading/listening process, answer source (whether the answer to the question was intratextual, extratextual, or intertextual), format, inference type, and teacher specificity. The study was designed to utilize a qualitative description method. Two fourth-grade teachers working in different primary schools were selected as participants. The data were collected through online lesson observations. A total of 56 lesson hours were observed and videotaped. The content analysis technique was applied for the analysis of the data. As in the results of previous studies, the present study, which was conducted in an emergency distance education environment, revealed that the text-based comprehension questions asked by teachers are generally asked after reading/listening, are answered with intratextual and extratextual sources, are prepared only in an open-ended question format, and are mainly of the propositional inference type. Additionally, it was found that teachers do not take the initiative to generate questions themselves and remain primarily dependent on the textbook.
In the context of the teaching and learning of reading comprehension, questions play a significant role in developing students’ higher-order thinking skills and ensuring in-depth understanding (Ateş 2011; Durkin 1978–79). They not only provide information to teachers about the academic status of students but also help students construct meaning from the text (Başaran 2019, pp. 295–296). According to Güneş (2021a), the primary function of questions is to inform and to be informed within the scope of learning and teaching (pp. 313–314). Questions can be used to make readers aware of the crucial points of a text (Day and Bamford 1998), to check comprehension (Gerot 2000), to extend the topic, to relate the text to prior knowledge and experiences to improve comprehension (Handsfield and Jiménez 2008) and to serve as an assessment of students’ comprehension of what has been taught (Kintsch 2005). Experts from the National Reading Panel (NRP) (2000) also argue that generating and answering questions effectively develops comprehension. In brief, questions can be considered an auxiliary element of students’ comprehension, meaning-making, and learning (Beck et al. 1996; Duke and Pearson 2002; Durkin 1979).
Text comprehension questions can be classified in many different ways using various parameters. For example, the revised Bloom’s taxonomy provides a framework for questions (Anderson et al. 2001). Questions related to the remembering stage of the taxonomy are directed towards recognition and recall. In contrast, questions associated with the understanding stage are directed towards interpretation, sampling, classification, summarizing, inferring, comparing, and explaining. The application stage includes execution and realization questions, while the analysis stage includes parsing, organizing, and examining questions. While questions related to the evaluation stage are about controlling/auditing and criticizing, the creation stage is about creating, planning, and producing (Krathwohl 2002). In Day and Park’s (2005) taxonomy, questions are divided into six comprehension categories. These categories are literal, reorganization, inference, prediction, evaluation, and personal response. Clymer (1968) mentioned that Barrett’s taxonomy puts questions into five categories. These categories are literal comprehension, reorganization, inferential/deep comprehension, evaluation, and appreciation. Furthermore, Akyol (2019) and Başaran (2019) suggested a question classification specifically for language lessons. They stated that it is necessary to classify questions based on their different aspects, including their purposes (questions about text schema and content), the timing of asking (pre-text, during-text, and post-text questions), the format (open-ended, multiple-choice, true-false, fill-in-the-blank, and matching questions), the cognitive processes required (simple comprehension and in-depth comprehension questions), and the answer sources [intratextual (the answer is within the text), extratextual (the answer is outside the text), and intertextual (the answer is between texts) questions]. In the current study, Akyol (2019) and Başaran’s (2019) question classification was preferred because it deals with different aspects of questions and is specific to language lessons.
Inference-making is essential for comprehension and is emphasized in the Turkish curriculum beginning at the 3rd-grade level (MoNE 2019). Inference is explained as obtaining the truth from a proposition with the help of other propositions, deducing a new proposition from a proposition (Hançerlioğlu 1989; Timuçin 1994). Questions can be utilized to lead students to make inferences. In this vein, there are various types of inferences that questions serve. Although there are different taxonomies (Nicholas and Trabasso 1980; Pearson and Johnson 1978; Warren et al. 1979) for inference, Chikalanga’s (1992) taxonomy was used in this study since it includes ideas from previous taxonomies and is more comprehensive. Accordingly, inference types are divided into lexical, propositional, and pragmatic inferences. Lexical inferences are aimed at finding the referents of pronouns and the intended meaning of words with multiple meanings (homophones) by using clues in the textual context. Propositional inferences are defined as necessary truths. This is because these inferences are directly derived from the semantic context of the explicit propositions in the text. Finally, pragmatic inferences are usually based on extratextual knowledge, in other words, the reader’s schemas (Chikalanga 1992). Examples of questions related to inference types taken from Kaya et al.’s (2021) study are given below:
“Bulut felt a hand touching his right shoulder while he was walking absent-mindedly. At first, he was scared, but when this fear was replaced by curiosity, he turned and looked behind him. It was his favorite friend, Mehmet Ali, whom he had not seen for years, touching his shoulder. As soon as he saw him, an enthusiastic smile covered his face.”
– To whom does the underlined word “him” refer in the above text?
Answer: Mehmet Ali
Inference type: Lexical inference
– Why was Bulut happy to see Mehmet Ali?
Answer: He was happy to see a friend he had not seen for years.
Inference type: Propositional inference
– Why do you think Bulut is walking absent-mindedly?
Answer: He might be thinking about something he has to do, or Bulut may typically be absent-minded and contemplative.
Inference type: Practical inference
In the comprehension process, the quality of the questions (type, content, comprehension level, and purpose) is among the factors that affect the construction of meaning (Fordham 2006a). For example, if the objective was to develop students’ in-depth comprehension skills, it would be inappropriate to ask simple comprehension questions (Cerdán et al. 2009; Fordham 2006a). This is because the answers to simple comprehension questions appear directly in the text as words or sentences. Simple comprehension questions require low-level cognitive skills. For in-depth comprehension questions, the answer is not directly mentioned in the text. It is either implied or found by using an individual’s background knowledge. In answering such questions, students need to activate high-level cognitive processes such as analysis, synthesis, and evaluation (Akyol 1997; Başaran 2019, pp. 307–308). Stevens (1912) observed that approximately two-thirds of the questions posed by teachers were based on memorization and recall. Similarly, both Ateş’s (2011) study examined the questions asked to fifth-grade students in Turkish (L1) lessons (Ateş 2011), and Özdemir’s study investigated questions in 1–5 grade level Turkish textbooks (Özdemir 2011) in Türkiye and found that they were mostly simple comprehension questions that require low-level thinking skills. To construct meaning and provide in-depth comprehension, teachers need to model how to make meaning through the questions they ask (Hervey 2006), and students need to be given opportunities to work with question types that engage different comprehension processes (Day and Park 2005).
One of the most common methods used in the teaching process is the question-answer method (Parker and Hurry 2007). When the related literature is examined, many studies are found that consider the use of questions in the teaching-learning process and during text-based comprehension activities (Afflerbach 2010; Akyol 1994, 1996, 1997, 2001; Akyol et al. 2013; Applegate and Applegate 2004; Applegate 2007; Applegate et al. 2002; Aslan 2011; Ateş 2011; Aydemir and Çiftçi 2008; Ayvacı and Türkdoğan 2010; Batur et al. 2019; Beck et al. 1996; Boyles 2003; Cerdán et al. 2009; Changpakorn 2007; Cortese 2004; Day and Park 2005; Demirgüneş et al. 2014; Durukan 2009; Eason et al. 2012; Fordham 2006b; Kreiner 1996; Langer 1985; Özdemir 2011; Parker and Hurry 2007; Polat and Dedeoğlu 2020; Raphael 1982; Rosenshine et al. 1996; Stevens 1912; Tuinman 1973; Ülper and Yalınkılıç 2010). In these studies, questions are classified according to their format, the cognitive processes required, the answer sources (whether the answer to the question is in intratextual, extratextual, or intertextual), or various taxonomies. These studies were generally conducted by analyzing documents such as textbooks, student workbooks, teacher guidebooks, or exam papers. In some studies, teachers or preservice teachers were asked to list the questions they would likely ask their students about a particular text through various data collection tools. Research on comprehension questions based on direct classroom observations (Ateş 2011; Durkin 1978–79; Stevens 1912) is quite limited. However, investigating the type, content, and level of comprehension questions through observations has the potential to make essential contributions in terms of revealing the details of the implementation process. To utilize this potential, in this study, the approach employed involved examining the comprehension questions of primary school teachers about the texts they teach in Turkish lessons based on observations.
Turkish (L1) lessons are taught based on a thematic approach in Türkiye (MoNE 2019). The thematic approach has emerged as an instruction model that involves students working on a specific theme in detail (Arhan and Gültekin 2013; MoNE 2019). In the thematic approach, themes provide opportunities for students to expand, organize, and structure knowledge and skills in the mind, and to transfer them to daily life (Güneş 2009). Texts are essential elements of the thematic approach (Arhan and Gültekin 2013). There are different types of texts (informative, narrative, poetry) in the Turkish textbook to help students acquire basic language skills. The curriculum predetermines the texts in the book. Eight themes are included in the Turkish textbook for one school year. For each theme, the textbook proposes four texts, three of which are reading texts, and one involves listening/watching (students do not read these texts but listen to audio recordings or watch videos). Each text addresses different subdimensions of a theme. For example, the “Science and Technology” theme is one of the themes in the fourth-grade Turkish textbook (Kaftan-Ayan et al. 2021). The first text in this theme provides examples of technological tools that can be invented. The second text explains how to make an invention. The third text presents examples of the life and inventions of an inventor, and the fourth one describes the characteristics that scientists should have. Thus, the “Science and Technology” theme is taught in different aspects. Various text-based activities are carried out with a student-oriented approach before, during, or after reading or listening to the text. The primary purpose of these activities is to ensure that the students understand the text. Indeed, according to many studies in the literature (Akhondi et al. 2011; Akyol 2018, p. 4; Block and Parris 2008; Duke and Pearson 2002; Durkin 1978–79; Güneş 2021b; Loranger 1997; Pressley 2002; Temur 2001, p. 62), the main purpose of reading and listening is comprehension.
One of the crucial factors that affects learning is the learning environment (Özerem and Akkoyunlu 2015). The integration of technology into learning environments and the transformation of educational processes have accelerated in recent years. However, the most rapid change in educational environments in many countries occurred during the COVID-19 pandemic. With the first COVID-19 case in Türkiye, the Ministry of National Education decided to close the schools on March 16, 2020. On March 23, 2020, asynchronous and synchronous distance education was implemented through the Education Information Network (EBA) platform, and applications such as Zoom, Microsoft Teams, Google Meet, and Adobe Connect were used to continue education and training activities when schools were closed due to the COVID-19 pandemic.
This sudden and radical transformation in the educational environment due to the COVID-19 pandemic had the potential to affect many variables, such as the teaching process, teacher‒student and student‒student interactions, classroom management, student achievement, and motivation. According to Wong et al. (2022), although the distance education process has shortcomings, it provides certain advantages, such as providing students with a flexible and personalized learning environment, allowing them to progress at their own pace, removing limitations of time and space, and saving money and time.
In this context, curiosity arises about how distance education affects the quality of comprehension questions asked by teachers. Thus, this research aimed to investigate comprehension questions about Turkish texts in the emergency distance education process. Addressing comprehension questions in the emergency distance education process can be considered a microlevel investigation focusing on a small part of teaching and learning. Microlevel investigation is an inductive path that focuses on a specific situation and helps researchers reach new ideas. Given that education will become more digitalized in the future, it is essential to consider how microlevel aspects, such as the characteristics and quality of the questions, will affect the teaching and learning process. Therefore, examining questions that have important functions in comprehension in a digital learning environment can provide new data and new perspectives on the subject. Even though the study was conducted during the distance education period, it is expected that the findings will still apply to teaching processes in the future. Accordingly, the main problem of this research is as follows:
What are the characteristics of the text-based comprehension questions asked by fourth-grade teachers in emergency distance education Turkish lessons?
Based on this main problem, answers to the following subproblems were sought:
For Turkish lessons taught under the emergency distance education process, the following questions were considered;
What is the distribution of the text-based comprehension questions in the reading/listening process (pre-text, during-text, post-text)?
What are the formats of the text-based comprehension questions?
What are the answer sources (intratextual, extratextual, intertextual) of the text-based comprehension questions?
What are the inference types (lexical, propositional, pragmatic) of the text-based comprehension questions?
What is the dependency of teachers on the textbook when asking text-based comprehension questions?
In this study, a qualitative descriptive approach was applied. Qualitative description (QD) is a research method that describes experiences or events in a rich and straightforward way without in-depth interpretation (Milne and Oberle 2005; Neergaard et al. 2009; Sandelowski 2000). QD studies have taken a naturalistic perspective and examine phenomena in their natural state (Kim et al. 2017). Basic QD is not as highly interpretive as other qualitative research designs, such as grounded theory and phenomenology (Sandelowski 2000). “The goal is to stay close to the surface of the data while capturing all the elements of that experience, and the inherent scientific rigor is a reflection of a researcher’s ability to achieve that goal” (Milne and Oberle 2005, p. 143). As explained in the introduction, text-based comprehension questions have been analyzed through studies conducted in face-to-face educational environments. This study, which tries to examine how this situation is realized in the online environment, aims to produce a straightforward description and comprehensive summary of the phenomenon of interest. Therefore, the study was designed with a QD approach.
Qualitative samples are typically small due to the emphasis on thorough interaction with participants, and the findings are not generalizable. In qualitative research, one of the most effective methods is to select individuals with particular knowledge and experience about the phenomenon being examined (Cresswell and Plano-Clark 2011). In addition, participants’ readiness and willingness to openly share their experiences and ideas are also important (Bernard 2002).
In this study, which was conducted during the pandemic, preliminary contact was made with the principals of eight primary schools to arrange the observation of online fourth-grade Turkish lessons. School principals were informed about the purpose of the study, and our request to work with volunteer teachers was conveyed. However, positive feedback was received from only three school principals. The other school principals stated that teachers did not volunteer because of the uncertainties of the pandemic, their unfamiliarity with the online teaching process, or their anxiety. Thus, this study was conducted with three volunteer fourth-grade teachers. A pilot study was conducted in one teacher’s classroom, and the main study was conducted in two classrooms. Accordingly, the participants of this study were selected by convenience sampling.
Fluent reading skills are emphasized as a focus on “learning to read” in the first years of primary school. However, there is a shift in focus to reading comprehension skills, or “reading to learn”, at the fourth-grade level. Thus, we decided to conduct this study at the fourth-grade level. Following ethical principles, participant identities were kept confidential, and teachers were given code names. The names given to the teachers were “Teacher A and Teacher B”. Demographic information about the study participants is shown in Table 1.
In the present study, video recordings were taken to prevent data loss in the observation of the questions asked by the teachers in the Turkish (L1) lessons for comprehension. In addition, the teacher questions were compared with the textbook questions for the subproblem that assesses the dependency of teachers on the textbook when asking text-based comprehension questions. Accordingly, the questions asked by the teachers were listed first. These questions were subsequently compared with the questions in the Turkish language textbook.
The study data were collected in April-May in the second semester of the 2020–2021 academic year. In this period, schools were closed to face-to-face education due to the COVID-19 pandemic, and distance education started. Thus, the observational data of the study were collected during online lessons on the Zoom platform. The data were collected by the first author of the study. First, a pilot study was conducted in a different class from the classes in which the main observations were made, and 16 h of Turkish lesson observations were conducted between January 11 and 22, 2021. Pilot observations were used to develop the observation form for question categorization, and the researchers were also provided with experience in managing the data collection process.
Within the scope of the study, the main observation data were collected from the online classes of two fourth-grade teachers (Teacher A and Teacher B). Before the observation, the themes and texts were determined by interviewing the teachers. Both teachers’ online classes related to texts on the same theme were observed. For this purpose, the theme of “Health and Sports” theme was selected. This choice was influenced by the fact that two of the four texts in the theme were expository, and two were narrative texts. The texts in the “Health and Sports” theme were taught in Teacher A’s class between April 22 and May 27, 2021, and in Teacher B’s class between April 30 and May 26, 2021. From the beginning to the end of the process, 34 lesson hours were observed in Teacher A’s class, and 22 lesson hours were observed in Teacher B’s class. However, not all of this time was spent on the texts, and different activities (such as grammar and evaluation) were also carried out.
Table 2 presents information about the text taught and the duration of the classes in which only text-based activities were carried out. Non-text activities such as grammar and evaluation activities were excluded.
To eliminate the testing/monitoring effect on teachers and students and to accustom them to the process, the researcher attended the classes and took recordings for a week before the main data were collected. These recordings were not used in the analysis. Before collecting the main observation data, teachers were asked about their weekly class schedules, and an observation schedule was created. The researcher recorded the lessons while turning off her video image and sound to avoid distracting the students.
The data of the study were analyzed using Weber’s (1990) basic content analysis approach. This approach is deductive. Preliminary codes are developed before data collection. Theoretical and empirical works construct a basis for analysis (Drisko and Maschi 2016). Accordingly, the preliminary codes of this study were developed by drawing on Akyol’s (2019), Başaran’s (2019), and Chikalanga’s (1992) studies prior to data collection and analysis. The “observation form for question classification” was created as the basis for the data analysis. This form consists of two parts. In the first part of the form, some information describing the lesson was recorded. These were the theme, text name, text type, school, class, teacher, and date. In the second part, the five categories in Fig. 1 and their codes were given.
Observation form for question classification.
As shown in Fig. 1, “the observation form for question classification” was designed to find answers for each subproblem. To develop this form, a literature review was conducted, and a draft form was created. This draft form was subsequently sent to five experts, one of whom was an expert in qualitative research and four of whom were Turkish language field experts. Necessary arrangements were made as a result of the suggestions. For example, the category name, initially “the originality of the teacher question”, was changed to “question source” in line with expert opinions. In addition, the code names in this category were changed from “original” and “textbook-dependent” to “teacher” and “textbook”. The pilot observation data were analyzed with the revised form. During these analyses, some changes were made to the form, and the finalized form was used. The codes used are explained in Table 3.
In the data analysis process, video recordings were transcribed first. The transcripts were coded in line with the subproblems. We also examined whether the questions were found in the textbook; thus, we determined whether the teachers depended on the textbook while asking questions. Regarding the four texts analyzed in the textbook, there were two pre-text questions before each text and 4 or 5 post-text questions after each text. Before the observation, the listening text “A Medicine Tale” was also listened to, and it was determined that there were three during-text questions. The researchers first coded the data individually based on the predetermined codes. The coding results were subsequently compared. If there were discrepancies in the coding, they were discussed, and a consensus was reached. The results of the analysis were subsequently defined and interpreted.
Rigour can be created in qualitative studies using various strategies (Milne and Oberle 2005; Neergard et al. 2009). This study’s rigour was ensured within the framework of the credibility, confirmability, dependability, and transferability principles of Bradshaw et al. (2017). For credibility (to establish rapport before commencing observation), three volunteer teachers were contacted by telephone. The purpose of the research, the duration of the observation, and the fact that one researcher would be a passive observer during the observation were explained. Additionally, prolonged engagement, data sources, and researcher triangulation were used to ensure credibility. An audit trail technique was used to capture the data collection and analysis process to ensure dependability. Description of demographics of participants, direct quotations from teacher questions, and external expert opinions contributed to confirmability. Sufficient data collection and analysis details were provided for transferability to recreate the study.
Within the scope of the research, first, approval was obtained from the *** University Ethics Commission dated November 24, 2020, number ***, stating that the research followed ethical principles. Then, official permission was obtained from the Ankara Provincial Directorate of National Education. After the official procedure, approval was obtained from the school administrators where the research was conducted, the teachers whose classes were observed, the parents of the students in these classes, and the students. While providing consent, the participants were told that participation in the research was completely voluntary and that they could withdraw from the study at any time. It was also stated that their identities would be kept confidential, and the data would not be shared with anyone other than the researchers.
In this part of the study, the characteristics of the questions asked by the teachers during the narrative and expository text-based activities are presented. The results that emerged from the analysis of the data are divided into five categories: (1) distribution of the question in the reading/listening process, (2) answer source of the question, (3) question format, (4) question inference type, and (5) teachers’ dependency on the textbook when formulating questions.
The questions asked by the teachers within the scope of expository and narrative texts were analyzed in three groups, pre-, during-, and post-text, according to their distribution in the reading/listening process. The analysis showed that the questions teachers A and B asked were similar according to their distribution in the reading/listening process. As shown in Table 4, when evaluated in terms of narrative and expository text types, the timing of the questions asked by both teachers in the reading/listening process was similar.
According to the results, teachers mostly asked comprehension questions after the text. Post-text questions served to make inferences and enabled the teacher to assess the extent to which the text had been understood by the students. Some examples of post-text questions are as follows:
“When do Nasrettin Hodja’s pains start?” (For Healing – Narrative Text)
“How can we see microscopic creatures?” (Mysterious Creatures – Expository Text)
Pre-text questions were mostly asked to activate prior knowledge and to make predictions. These questions are generally included in the “preparatory studies” section of the textbook. This section, which consists of two questions, is provided before each text in the textbook. The questions in this section are asked to activate students’ prior knowledge about the text’s subject and enable them to make predictions about the text. For example, the question “What do you understand a balanced diet to mean?” which was posed to the students before reading the text titled “For Healing”, dealing with the subject of healthy nutrition, and the question “Why do plants and food decay?”, which was asked before the text “Mysterious Creatures”, addressing the topic of microscopic organisms, are among those posed to students to activate their prior knowledge. In addition, in the text titled “Our First Wrestler Koca Yusuf and the Secret of His Success” the question “What do you think the subject of this text could be?” was posed to the students based on the title and visuals of the text, was a prediction question.
Students were rarely asked questions while reading or listening to the text. As in the pre-text phase, the questions asked during reading were also oriented towards predicting and activating prior knowledge. These kinds of questions were asked only in the listening text “A Medicine Tale”. However, these questions were not directly asked by the teachers; rather, they were found in the audio recording of the text listened to by the students. Within the scope of participatory listening activities, while the students were listening to the text, the text was interrupted at a certain point, and questions were asked to predict what would happen next. For example, after listening to a part of the text titled “A Medicine Tale”, Elif, the main character of the text, asks Uncle Arda, another character, “How do medicines harm people? Why do people use them if medicines are harmful?”. The questions were directed to the students through the question “How do you think Uncle Arda answered this question?”. Thus, a participatory listening activity was carried out, which maintained students’ curiosity and motivation regarding the text. No questions were asked during reading in the other texts analyzed in this study.
The questions asked by the teachers during the text-based activities were divided into three groups: intratextual, extratextual, and intertextual. These analyses revealed that both teachers asked questions with similar characteristics in terms of answer sources. The teachers asked intratextual and extratextual questions in approximately equal amounts. However, in our dataset, it was determined that the teachers did not include intertextual questions. Table 5 shows the distribution of the questions asked by the teachers according to the answer sources.
The answers to the intratextual questions the teachers asked are directly included in the text. These questions are simple to answer. In the present study, the participants asked intratextual questions after reading/listening but not during participatory reading/listening. Since the questions asked before and during reading/listening in the analyzed texts are aimed at activating prior knowledge and predicting the content of the text, these questions are generally not suitable for answering with intratextual resources. For this reason, all the intratextual questions were post-text questions, as in the examples below.
“What do scientists do to find medicines?” (A Medicine Tale – Narrative Text)
“What does Mümin’s master, Yolcu Mehmet Pehlivan, say about Koca Yusuf?” (Our First Wrestler Koca Yusuf and the Secret of His Success-Expository Text)
On the other hand, teachers asked questions that could be answered with extratextual sources at all stages of the reading/listening process. Although mainly after the text, extratextual questions were also asked before and during the text. Since the entire text had not yet been read, the questions asked before and during the text were mostly answered with extratextual sources. For example, the question “What should we do to protect our health?”, which was asked before the text “Mysterious Creatures”, and the question “How do you think medicines cure diseases?”, which was asked during the text “A Medicine Tale”, are among the questions answered with extratextual sources. The extratextual questions asked after reading/listening are questions whose answers are not directly included in the text and which students can answer using their prior knowledge and experiences. The question “What are the solutions you have found to solve the problem in the text?” was asked after reading the text “For Healing”, which was a question that students could answer based on their prior knowledge and experiences. Therefore, the question could be answered with extratextual answer sources. In addition, neither teacher included intertextual questions in the “Health and Sports” theme that was analyzed within the scope of the research.
The questions asked by the teachers were divided into five formats: open-ended, multiple-choice, true/false, fill-in-the-blank, and matching questions. The table below shows the distribution of the questions asked by teachers in this context Table 6.
Both teachers included only open-ended questions in both text types. When the questions in the textbook were analyzed, the results were similar. The textbook and teacher-made questions did not include multiple-choice, true/false, fill-in-the-blank, or matching-type questions.
The questions asked by the teachers to comprehend the text were categorized into three groups in terms of inference type: lexical, propositional, and pragmatic. Table 7 shows the distribution of the questions asked by teachers to students according to inference type.
There were similarities between the number and distribution of inference questions the teachers asked. The teachers did not include lexical inference questions in either text type. It was also found that the textbook did not include such questions. Lexical inference questions are aimed at finding the referents of pronouns and the intended meaning of words with multiple meanings (homophones) by using context clues.
Observations show that teachers ask questions that address propositional and pragmatic inference types. In this process, propositional inference questions were used most frequently, as seen in the following excerpt taken from the narrative text “For Healing” in the textbook.
“Nasrettin Hodja fell ill for a while. The doctor tells him to abstain for a while and not to eat heavy and spicy food. He gives him medicines. Hodja follows what the doctor says for a few days. He feels improvement in himself. He craves spicy stuffed dishes….”
The questions given below for this text are examples of propositional inference:
“Why does the doctor ask Nasrettin Hodja to abstain?” (Logical-explanatory inference)
“What does Nasrettin Hodja want to eat when he gets better?” (Logical-informational inference)
Pragmatic inference questions were also asked by teachers in the text-based activities. The answers to such questions are not directly included in the text but are based on students’ prior knowledge and schemas. Therefore, pragmatic inference questions are similar to extratextual questions in terms of their answer sources. One example of a pragmatic inference question is “What is the importance of nutrition for an athlete? Talk about it as a class.” To answer this question, students need to have some prior knowledge and schemas about the subject. Students who do not have prior knowledge about balanced and regular nutrition, energy requirements, nutritional values of foods, and exercise may not be able to provide a qualified answer to this question. Another example of this type of question concerns a visual activity in the text “For Healing”. In this visual activity, Güven Usta suggests a correct sitting position for a child who is not sitting upright at a desk. In line with this suggestion, students are asked, “How can Güven Usta’s suggestion benefit our health in the future?”. To answer this question, students need to have prior knowledge and schemas on topics such as spinal structure, the muscular system, and the circulatory system. Otherwise, the answers given may not be sufficient.
As stated earlier in the present study, intratextual questions according to answer sources were similar to propositional inference questions in the classification made according to inference types, while extratextual questions were similar to pragmatic inference questions. Based on these findings, a detailed analysis was conducted. The code co-occurrence model related to the categories of inference types and answer sources was examined. The connections between the categories are presented concretely with numerical values in Fig. 2.
Code co-occurrence model according to the answer sources and inference types of the text-based comprehension questions.
As shown in Fig. 2, almost all questions of the propositional inference type can be answered with intratextual sources. On the other hand, all of the questions of the pragmatic inference type can be answered using extratextual sources. The participants in this study did not ask any questions about lexical inference type, which is one of the inference types, or intertextual type, which is one of the answer sources. Therefore, further analysis of these codes could not be conducted.
It was determined that both teachers mostly followed the textbook in the text-based activities. In addition, we analyzed whether the teachers created unique questions without being dependent on the textbook. The results are summarized in Table 8.
Both teachers asked text comprehension questions based on the textbook without differences in type of text. Although the proportion of unique questions produced by Teacher A was greater than that produced by Teacher B, approximately 90% of the questions were textbook-based.
This study aimed to examine the text-based comprehension questions that fourth-year teachers asked in Turkish lessons taught during the period of emergency distance education that was implemented during the COVID-19 pandemic. The first finding of this study is that the participating teachers asked more post-text questions than pre- and during-text questions. This result is similar to those of studies conducted in face-to-face education environments (Akyol 2001; Kaya-Özgül and Çetinkaya-Özdemir 2022; Sunggingwati and Nguyen 2013). This finding implies that teachers’ text-based comprehension questioning did not change during the distance education period. The teachers asked all pre-text questions determined for the texts analyzed in the Turkish language textbook and the questions in the listening text. This behavior of the teachers shows that they tend to adhere closely to the textbook. Teachers’ adherence to the textbook is understandable because textbooks are seen as the primary source of educational material in Türkiye.
Another issue addressed in this study is the type of questions asked by teachers in the distance education period, which were categorized by answer source. It was observed that teachers asked intratextual and extratextual questions almost equally in the text-based activities. This result is compatible with previous studies performed in face-to-face teaching (Akyol 2001; Akyol et al. 2013; Kaya-Özgül and Çetinkaya-Özdemir 2022). While the teachers asked extratextual questions to activate prior knowledge and prediction, they did not ask high-level cognitive questions that involved inferring, evaluating, or making connections. The Turkish language curriculum emphasizes the importance of developing higher-order cognitive skills such as inferring, evaluating and making connections from the third grade onwards (MoNE 2019). However, the results of the research conducted in face-to-face settings and this study reveal that teachers do not ask such questions. This important finding suggests that teachers’ awareness of the Turkish language curriculum and the reasons for not asking such questions are issues that need to be investigated. Another result related to the answer source is that the teachers did not include any intertextual questions in text-based activities, as found in other studies (Akyol 1994, 1997; Ateş 2011; Kaya-Özgül and Çetinkaya-Özdemir 2022; Sunggingwati and Nguyen 2013). The reason why teachers do not ask intertextual questions may be that there are no such questions in the Turkish language textbook. However, there are explanations in the Turkish language curriculum that encourage teachers to ask questions that lead students to construct intertextual meaning. According to the curriculum, by studying different types of texts within the scope of a theme, students are expected to construct relationships between these different types of texts (MoNE 2019).
As has been found in various studies (Akyol 2001; Fordham 2006a; Kaya-Özgül and Çetinkaya-Özdemir 2022; Sunggingwati and Nguyen 2013; Şengül 2005), both teachers included only open-ended questions in both text types intensively in this study. When the questions in the textbook were analyzed, it is noteworthy that the situation was the same. Multiple-choice, true/false, fill-in-the-blank and matching-type questions were not included in the textbook or teacher-made questions. In some studies (Akyol 2001; Şengül 2005), open-ended questions were called “classical questions”. Hynds (1990) argued that teachers should enable students to construct meaning from texts with open-ended, multifaceted, and student-centered questions. Since closed-ended questions have limited answers (Akyol 2019), if teachers limit meaning-making to short-answer questions, they can limit what their students can learn about literary works and reading texts (Hynds 1990). Thus, teachers’ use of open-ended questions can be considered positive. However, as stated by Şengül (2005), when all questions that are posed are open-ended, as a result, students may be unfamiliar with different question types.
When the text comprehension questions asked by the teachers were analyzed in terms of inference type, similar results were obtained regardless of the text type. Propositional inference-type questions were mostly used. These questions included pragmatic inference questions. This result is similar to the results obtained from Demirgüneş et al.’s (2014) study in which the questions in the 8th-grade Turkish (L1) teacher’s guidebook were classified according to inference types. However, it contradicts the results of Kaya et al.’s (2021) study in which questions in middle school textbooks were analyzed according to inference type. According to the results of the mentioned study, mostly pragmatic inference questions were included in the textbooks, followed by propositional inference questions. These results show that as grade level increases, the types of inference can be differentiated and higher level inference questions can be asked. In addition, the difference between the results of Demirgüneş et al. (2014) and Kaya et al. (2021), although both studies were conducted with middle school textbooks, can be attributed to the changes in curricula and textbooks over time, considering the publication years of the studies. In studies examining students’ inference-making skills (Algül and Bozkurt 2021; Bayat and Çetinkaya 2020), it was observed that pragmatic inference-making was the most difficult for students. According to Kaya et al. (2021), such difficulty in pragmatic inference questions shows that students fail to use their world knowledge and experiences to make connections between texts. This finding also suggested that students cannot internalize texts. For this reason, it is essential to include pragmatic inference questions to a sufficient extent. According to the results of this study, more propositional inference questions were asked than other question types in text-based activities. Propositional inference questions are answered by interpreting the semantic content of the text. These questions require logical informational and logical explanatory inferences. To answer such questions, logical-semantic relationships need to be established between the sentences or paragraphs that make up the text. According to the results of the study, lexical inference questions were not included by the teachers, nor were such questions encountered in the relevant sections of the textbook. Lexical inferences serve to identify and monitor pronoun referents and to ensure the continuity of the subject in the text. They are also effective at finding the meaning of words in the text context (Demirgüneş et al. 2014). Given this explanation, teachers’ failure to include such questions was considered a deficiency by the researchers.
Another issue the study addressed was teachers’ dependency on the textbook when asking questions about text-based activities. When the questions were analyzed, it was seen that teachers were almost entirely dependent on the textbook when asking questions, regardless of the text type. These findings are similar to those of other studies (Akyol et al. 2013; Alvermann and Phelps 2002; Dillon 1988; Shomoossi 2004; Sunggingwati and Nguyen 2013; Van Der Meij 1994). There may be various reasons why teachers are almost entirely dependent on textbooks when asking questions and rarely ask questions of their own making, and this topic can be explored in new research studies.
This study was conducted with two teachers in 56 lesson hours during the COVID-19 pandemic. Therefore, the data are relatively limited. However, this approach also contributes to determining whether there are changes in the characteristics of the text-based comprehension questions teachers ask in distance education. One of this study’s most important results is that teachers depend on the textbook instead of producing text-based questions themselves. In general, further research to discover teachers’ views on why they had chosen to use almost exclusively questions they had not created can be performed to contribute to studies on questioning and comprehension. In addition, additional research should be conducted on why teachers usually ask post-text questions that are to be answered with intratextual and extratextual resources, are prepared only in the open-ended question format, and mainly serve the propositional inference type but not higher-order comprehension. Considering these results, training can be given to teachers on the characteristics of the questions to be asked in text-based activities, asking a variety of questions that address individual differences and issues to be considered in the question-answer method. The question-answer method is but one possibility for comprehension. It is also a hierarchical method that does not allow children to learn in different ways. Whether teachers use this hierarchical method in the classroom through questioning could also be an interesting research topic. Future studies can examine other teaching-and-learning-methods that can be combined with reading.
The raw observation records of the current study cannot be shared to keep the identity of the participants confidential in accordance with ethical principles. However, the data analyzed during the current study is submitted as a supplementary file (in Turkish) and can also be obtained from the corresponding author upon reasonable request.
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Gazi University, Gazi Faculty of Education, Ankara, Türkiye
Ülkü Çoban Sural & Naciye Aksoy
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The authors confirm contribution to the paper as follows: study conception and design: [Ülkü Çoban Sural, Naciye Aksoy]; data collection: [Ülkü Çoban Sural]; analysis and interpretation of results: [Ülkü Çoban Sural, Naciye Aksoy]; draft manuscript preparation: [Ülkü Çoban Sural]; review and editing; [Naciye Aksoy]; revision: [Ülkü Çoban Sural, Naciye Aksoy]. All authors reviewed the results and approved the final version of the manuscript.
Correspondence to Ülkü Çoban Sural.
The authors declare no competing interests.
This study was approved by the Ethics Committee at Gazi University on November 24, 2020 with approval number E.126119.
Written informed consent was obtained from participants before observations. As the authors of this paper, we undertook not to include any personal identifying details of the participant, as well as video records.
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Çoban Sural, Ü., Aksoy, N. Investigation of text-based comprehension questions in emergency distance education during the COVID-19 pandemic. Humanit Soc Sci Commun 11, 716 (2024). https://doi.org/10.1057/s41599-024-03194-z
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The horror of a school shooting struck the Madison area for the second time in seven months on Monday, when police say a teenager shot eight people before taking her own life at a private Christian school.
In total, a student and teacher were killed and six people were injured. Two of them are in critical condition with life-threatening injuries, and four suffered injuries that weren’t life-threatening, Madison Police Chief Shon Barnes said.
Barnes identified the suspected teen shooter Monday night as 15-year-old Natalie Rupnow, who went by Samantha, and was a student at the school. She was found with an apparent self-inflected gunshot wound and died while in transport to a hospital, the chief added.
The shooting took place inside a study hall filled with students from different grades, Barnes said. The chief originally said Monday that a second grader was the first person to call 911, but clarified Tuesday that the caller was a second grade teacher.
Shock and grief reverberated across Wisconsin as news of the shooting at Abundant Life Christian School spread Monday. As police methodically cleared the K-12 school, which sits on a 28-acre campus with a preschool and a church, city buses took students to a clinic down the road serving as a reunification point. Some children peered out the bus windows, waving to news crews gathered near the school. Others appeared somber.
As they arrived at the clinic, parents ran inside to meet their children. Some were seen carrying out infant car seats. One child left the clinic with his parents wearing a Santa hat, a reminder that the shooting rocked a school community preparing for Christmas. Most parents declined to speak publicly.
“This hits you in the gut,” Dane County Executive Melissa Agard said. “Every one of us is affected by this unimaginable and unacceptable violence.”
At 10:57 a.m., dispatchers received a call of an active shooter at the school at 4901 E. Buckeye Road. By 11 a.m., officers arrived at the school, Barnes said. Authorities found Rupnow dead and also recovered a handgun, Barnes said.
Rupnow appears to have been in school before the shooting began and did not “breach” the school’s locked entryway, Barnes said.
Barnes also did not provide the ages and genders of the injured victims. He said they included one teacher and five students. Two injured individuals had been released from the hospital, Barnes said Monday evening.
“My heart is heavy for my community. My heart is heavy for Madison,” Barnes said. “We have to come together as a community and figure out what happened here, and make sure that it doesn’t happen in any other place that should be a refuge for students in our community.”
He said he didn’t know why the teen opened fire but that police were searching her home.
“If we did know why, we could stop these things from happening,” Barnes said.
Police officers did not fire their guns during the incident, Barnes said.
Abundant Life did not have a school resource officer or metal detectors. Cameras in the building are monitored, doors remain locked and every student is “visually scanned” by staff as they arrive at school, said Barbara Wiers, director for elementary and school relations for Abundant Life.
Of the eight people who were shot, four people were transported to SSM Health St. Mary’s Hospital, and three were transported to University of Wisconsin hospitals, according to Madison Fire Chief Chris Carbon. One of those victims died on the way to the hospital and one of the gunshot victims was pronounced dead at the scene, Barnes said.
Roads were blocked off along 4900 Buckeye Road. Police asked people to avoid the area.
Police are searching the home of the suspected shooter, Barnes said, and the parents are cooperating and agreed to let authorities search the home.
President Joe Biden said the shooting was “shocking” and “unconscionable” and called for congressional action.
“From Newtown to Uvalde, Parkland to Madison, to so many other shootings that don’t receive attention – it is unacceptable that we are unable to protect our children from this scourge of gun violence,” he said in a statement. “We cannot continue to accept it as normal. Every child deserves to feel safe in their class room. Students across our country should be learning how to read and write – not having to learn how to duck and cover.”
He called on Congress to pass universal background checks, a national red flag law and a ban on assault weapons and high-capacity magazines.
About 420 students are enrolled in the school from 3-year-old kindergarten to 12th grade, Wiers said. It shares a 28-acre campus with Campus for Kids Learning Center, a program for infants through 4-year-old kindergarten, and the nondenominational City Church.
Shortly after the shooting, a Journal Sentinel reporter observed parents running into a nearby SSM Health urgent care and clinic to retrieve children, some of whom were in infant car seats. Located at 1821 S. Stoughton Road, it served as a reunification point for parents. Ambulances were seen leaving the area of the school periodically.
By midafternoon, as the school was cleared by police, several city buses left the school, each with a handful of students in the back, and several police officers in the front, then arriving at the reunification point. The reunification process continued for hours and was completed by early evening.
At the school, two dozen police cars were parked with their red and blue lights flashing.
Parent Bethany Highman said her daughter goes to school at Abundant Life.
Highman declined to give her daughter’s name or age. She said she video-called her child briefly earlier in the day to learn she was OK.
She was uncertain of the next steps but said she intended to support her daughter through community and prayer.
“Your world stops for a minute. Nothing else matters,” she said. “We’re just waiting, praying.”
Troy Regge lives about three blocks from the school. Many in the neighborhood send their children to the school, he said.
“Nothing like this has ever happened in the area,” he said. “It’s a sad situation all around.”
At a Kwik Trip near the school, Shannon Moungey of Stoughton reflected on the shooting.
It’s “a very sad situation this close to Christmas,” she said. “Kiss your loved ones tonight extra, extra.”
Moungey said she was surprised it happened in Madison.
“It’s a little close to home,” Moungey said. “You see more and more of this everyday, and it’s very unfortunate. You see this on TV and you never think it’s going to happen in your area, and unfortunately it did for us today.”
Moving on will be “a long process for many, I think,” she said.
According to the Madison Metropolitan School District, La Follette High School and Sennett Middle School are on secure hold due to the shooting. Barnes said there are no threats to Madison public schools at this time.
Agents from the U.S. Bureau of Alcohol, Tobacco, Firearms and Explosives are working to trace the gun. Barnes said he was in contact with the FBI as well.
Earlier in the day, the Green Bay Area Public School District announced on social media that police were investigating potential shooting threats at two of its four high schools. As of noon, three persons of interest had been identified, and it was determined there was no ongoing threat. Police are still actively investigating.
Abundant Life’s official Facebook page released a short statement, asking for prayers after the shooting. And City Church, which is affiliated with the school, asked for the public not to contact them.
The church said it would hold a meeting Tuesday night to pray school staff, students and families.
Abundant Life was founded in 1978 with 30 students with a mission to “train young men and women in the development of Christian character and academic excellence.” Today, its students come from about 200 families and 56 different churches in the Dane County area.
City Church is also a partner of Christian Life College-Madison and the Christian radio station 97.1-FM. The church is affiliated with the Fellowship of Christian Assemblies, a Pentecostal association.
Gov. Tony Evers acknowledged the shooting in a statement, saying his office was monitoring the situation.
“We are praying for the kids, educators, and entire Abundant Life school community as we await more information and are grateful for the first responders who are working quickly to respond,” Evers said.
U.S. Sen. Ron Johnson reacted to the shooting on X: “My sincere condolences and prayers for all the victims of the tragedy at Abundant Life Christian School. I will continue to closely monitor the situation.
And Democratic U.S. Rep. Mark Pocan replied to a message from U.S. Rep. Derrick Van Orden on X, saying in part: “Support better gun laws or stay out. Thought & prayers are useless. Action!”
State Superintendent Jill Underly issued her second school shooting statement in a seven-month period. As she emphasized in her first statement in Mount Horeb, she repeated: schools are meant to be safe places.
“Our hearts are heavy with great sorrow as we mourn the senseless loss of life in yet another school shooting,” Underly wrote Monday. “No community should ever face the pain of losing children, teachers, or friends to violence – especially in our schools.”
Underly underscored the need to “do more to protect our children and educators” and present a future “where senseless acts of violence are no longer a reality.”
In May, a 14-year-old student brought a Ruger .177-caliber pellet rifle to Mount Horeb Middle School where he went to school. The student pointed the gun at officers, who was fatally shot after being told to drop his weapon. He was the only casualty, with no other physical injuries reported.
The shooting rocked the small community in Dane County, located 25 miles west of Madison. The events left many with tricky questions over what exactly transpired, the boy’s intentions and whether his death could have been prevented.
Claire Reid, Madeline Heim, Natalie Eilbert, Alec Johnson and Drake Bentley of the Milwaukee Journal Sentinel and Nadia Scharf of the Green Bay Press-Gazette contributed to this report.
This story will be updated.

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December 18, 2024 05:20 ET  | Source: Polaris Market Research & Consulting LLP Polaris Market Research & Consulting LLP
New York, USA, Dec. 18, 2024 (GLOBE NEWSWIRE) — Artificial Intelligence Market Outlook:
The market for artificial intelligence is poised to witness robust growth, according to the latest research report by Polaris Market Research. The artificial intelligence market size was valued at USD 235.27 billion in 2024 and is projected to grow to USD 3,582.75 billion by 2034. It is anticipated to exhibit a CAGR of 31.3% from 2025 to 2034.
What Is Artificial Intelligence?
Artificial intelligence (AI) is a set of technologies that allow machines and computers to simulate human learning and reasoning. AI is a broad field that draws from several disciplines, including neuroscience, statistics, computer science, and philosophy. AI systems can learn from their errors and improve their accuracy. They can understand and translate spoken and written language. Also, AI systems are capable of analyzing vast amounts of data to make recommendations. AI finds applications across various industries, including healthcare, e-commerce, space exploration, education, marketing, and security systems, among others.
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Who Are Top Market Players?
The top market participants are focusing on R&D to improve their product offerings. Also, they are adopting several strategic initiatives to improve their market share.
A few of the artificial intelligence market key players are:
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What’s Driving Market Forward?
Availability of Historical Datasets: The availability of historical datasets has led to increased innovation in AI. With data storage and recovery becoming more economical, governments and organizations are developing unstructured datasets that can be easily accessed by research. This availability of historical datasets is driving the artificial intelligence market development.
Advancements in Artificial Neural Networks: Advancements in artificial neural networks (ANN) have resulted in increased adoption of AI across several sectors, including automotive, healthcare, manufacturing, and aerospace.
Continuous Research and Innovation: The top market participants are continuously focusing on continuous research and development to drive the adoption of advanced technologies, including AI. This increased focus on research and innovation is driving the artificial intelligence market expansion.
Which Region Leads Market Demand?
North America: North America held a sizable portion of the artificial intelligence market share in 2024. The introduction of favorable regulations and government initiatives has led to increased adoption of AI across various sectors. Also, significant investments in AI research and development contribute to the market dominance in the region.
Asia Pacific: Asia Pacific is projected to register a substantial CAGR from 2025 to 2034. Several emerging economies in the Asia Pacific have introduced steadfast plans for the adoption of AI technology across various industries. Also, the AI use cases in Asia Pacific are projected to rise as 5G technology is being increasingly implemented in nations such as China, India, and Japan.


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A few days ago, the team of the famous memecoin Shiba Inu announced to the public the arrival of a new protagonist within its ecosystem, namely the token Shifu.
The currency will be officially released this week, accompanied by an airdrop in favor of the holders of SHIB, BUBBLE, LEASH, and other resources.
A public presale has also been organized, which everyone can freely participate in, but it will expire in a few hours.
We delve into everything in this article.
Summary
The community of Shiba Inu will soon host a new companion from Japanese meme culture, already known to the public as Shifu token.
The news had been anticipated a few days ago by the influencer Lark Davis, then confirmed by Shytoshi Kusama, lead developer of the famous memecoin.
Shifu was born from a collaboration between the Shiba Inu team and that of Imaginary Ones, a blockchain platform that integrates digital art, games, and NFT.
This partnership makes the new token unique and innovative, being integrated with various gamified elements of the web3 world.
The name Shifu, which in Japanese means “master”, evokes the image of a master who guides and teaches, bringing discipline and wisdom to the chaotic market of memecoin.
The design of the new coin is very reminiscent of the elderly master from the film “Kung Fu Panda,” also called Shifu. 
Presenting the token to the crypto markets, it was also revealed that there will be a reward for all supporters of the Shiba ecosystem. The official launch is scheduled for this week
#SHIB #Shibarmy #LEASH LFG

Did someone say Airdrop? https://t.co/pJd44aGKaY
As anticipated by the influencer Davis, the launch of the Shifu token will be accompanied by a large airdrop to the Shiba and Imaginary ones communities.
All holders of SHIB, BUBBLE, LEASH, BONE and the owners of the NFT Shiboshi, Sheboshi, and Imaginary Ones Carnival will be eligible.
We do not know exactly when the snapshot will be taken, but it is likely that it has already been taken.
In total, 30% of the supply of the new Shifu token will be allocated for the airdrop, aiming to reward and strengthen the cohesion of a successful meme ecosystem.
More specifically, 22% will be offered to all users who at the time of the snapshot have a balance of at least 100,000 SHIB and 100 BUBBLE.
The 4% is reserved for the holders of Shib Games, which include the tokens LEASH, BONE, and the NFTs Shiboshi and Sheboshi.
The remaining 4% is instead allocated for the Imaginary Ones Carnival participants and its related NFTs.
The currency will be released on the blockchains of Ethereum and Shibarium, with the “Token Generation Event” scheduled in the coming days.
This airdrop is designed to reward existing supporters and encourage the adoption of the new token among the loyal users of the platform.
SHIFU, a groundbreaking partnership between Shiba and Imaginary Ones, is bringing an incredible airdrop exclusively for our loyal Shib community! 🌟

Here’s how the airdrop supply will be distributed:
22% Supply: Hold 100,000 $SHIB + 100 $BUBBLE to qualify.

4% Supply: Reserved… pic.twitter.com/4cp5UieUzs
Introducing the new Shifu token, the Shiba Inu team also revealed its tokenomics, which is as follows.
The total supply is 1,000 billion units, with 50% to be released at the TGE and the remaining 50% unlocked over four weeks.
This gradual release strategy aims to prevent excessive market volatility and to ensure a smooth initial transition for new holders.
As anticipated in the paragraph, 30% of the supply of Shifu is reserved for airdrop purposes, with 30 billion tokens that will end up in the wallets of the Shiba ecosystem.
25% of the supply, or 25 billion Shifu, will be used to provide liquidity on various centralized and decentralized exchanges.
This is a fundamental step to ensure that there are enough resources available to facilitate trading in the market, reducing initial volatility.
Obviously, like all memecoins, an allocation dedicated to the project’s marketing could not be missing: 10% of the existing Shifu will be used to pay crypto influencers and other collaborators who will help make the new meme popular.
Another 20 billion tokens (20%) will be reserved in the project’s treasury, with the money being used to support the long-term growth of Shifu by funding any future developments.
Finally, 15% of the supply, that is 15 billion tokens, will be made available for a public presale. This will allow all interested investors to purchase Shifu before it is launched on the market.
As just described, the Shifu token will be initially distributed not only to the Shiba community but also to all investors who will participate in the public presale.
Anyone will have the opportunity to purchase Shifu at an initial market cap of 2.25 million dollars (575 ETH), with a FDV cap of 15 million (3830 ETH).
To participate, it is sufficient to send a minimum of 0.1 ETH (there is no maximum cap) to the following address: 0x0Ec5b7fe8F8f13C57daCf2B617Deff1640911516
Only transfers from DEX wallets, such as MetaMask and Rabby, will be accepted, while those coming from CEX will be discarded and sent back.
15% of the supply is openly sold in this phase, with 100% of the funds being used for liquidity purposes.
The official launch is expected in the coming days. The presale ends today at 15:00 UTC.
The Shifu team explained that in case of overwrites, the tokens will be adjusted proportionally and the excesses will be refunded.
At this moment, the cap set at 575 ETH has already been significantly exceeded, as over 2,220 ETH have been deposited.
Indicatively, the participants in the presale will purchase the Shifu token at an FDV of 60 million dollars.
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Tomarket, a prominent Telegram-based crypto game, is rolling out its TOMA token on The Open Network (TON) with a series of airdrop events planned for its player base.
The token is expected to begin trading on Dec. 20, following its initial airdrop event which occurred on Oct. 31.
The airdrop is divided into three phases, with 80% of the total token allocation designated for players. The first phase revealed 30% of the tokens on Oct. 31, while another 30% will be disclosed before the listing date.
To be eligible for the airdrop, players must complete several tasks, including adding a tomato emoji to their Telegram display name, reaching Bronze (L4) level in the game, connecting a TON wallet, and joining the Tomarket Announcement community channel.
Eligible players will reportedly claim their tokens through the Tomarket mini app on Telegram and can choose to withdraw them to their wallets.
However, only those who purchase the premium medal are promised full access to their TOMA allocation at launch. Non-buyers may receive a portion of their tokens initially, with the rest vesting over time.
I'm just your average dog… Only decentralized; also… I'm not your average dog.
Join the thousands already learning crypto!

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Opinion | I Was a Health Insurance Executive. What I Saw Made Me Quit.  The New York Times
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By Merrill Goozner
Dec. 18, 2024
Goozner is the former editor-in-chief and columnist at Modern Healthcare.
Online vitriol isn’t pretty. But the mass schadenfreude that greeted the assassination of United Healthcare CEO Brian Thompson, while cruel and inappropriate, did succeed in drawing attention to growing rage over the nation’s private health insurance system.
Consumers are angry because the premiums for their health insurance are too high. Patients are angry because they pay too much out-of-pocket for health care when they use their insurance. Both patients and their doctors are angry when they are denied services by a distant bureaucrat working for a health insurer.  
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Among all advanced industrial countries, the U.S. goes furthest in using premiums, copays, and deductibles to influence access to care. Proponents argue that making patients have more “skin in the game” incentivizes them to stop purchasing unnecessary care and lower overall health care spending.
It has never worked. Studies have repeatedly shown that patients respond to high out-of-pocket costs by avoiding necessary and unnecessary care in equal measure. Avoiding necessary care is one reason why the U.S. has worse health outcomes than many nations that spend much less on health.
It is time to put an end to this failed experiment. If we want a humane health care system that delivers better outcomes, we must design payment policies that serve patients, not the insurance industry.
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Where to start? Government must set a hard cap on the share of household income that can be spent on premiums, copays, and deductibles in any given year. This simple, easily enacted reform will make using health insurance affordable and put an end to medical debt.
To pay for this reform, payers in both the public and private sectors must change how they reimburse hospitals and doctors. The current system is almost entirely fee-for-service. It needs to change to guaranteed annual budgets for hospitals and physician practices with strict rules governing what services must be delivered.
Meeting patient needs while staying within a budget can only be achieved if providers control the purse strings. Surely a country that spends 50% more than any other on health can meet every citizen’s health care needs if providers have the flexibility to efficiently allocate resources.
Under guaranteed budgets, hospital systems and physician practices will be able to redirect personnel and financial support toward primary care, prevention, and addressing patients’ social needs, which will hold down costs. It also gives providers the power to drive down prices, both internally (by eliminating administrative waste and better use of personnel) and from external suppliers like the drug, device, test and imaging equipment makers. The Veterans Administration, which operates the nation’s largest health care system, operates within a guaranteed annual budget.
Under the current system, most hospitals, physicians, and their suppliers (including drugmakers) receive a payment for every service or product they deliver. Like any piecework system, the more they deliver and the higher the price, the more they make.
Conversely, insurers are incentivized to deny care under fee-for-service medicine. The less they allow, the more they make. The easiest way to pad profits is to hold down utilization through tactics like prior authorization.
The irony is that insurers are already paid a lump sum for each enrollee by employers, Medicare and Medicaid (more than half of Medicare and nearly three-quarters of Medicaid beneficiaries are now in insurer-run plans). This system gives control of the money to the wrong party.
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Giving hospitals and doctors the power to determine how our health care dollars get spent is not single payer. Both government programs and private insurers will contribute to the annual budgets, whose growth can be adjusted by state or federal regulators for inflation, demographic changes, emergency health challenges like pandemics, and new technology.
Unacceptably high out-of-pocket costs are not a new problem. When Medicare was created in 1965 to cover the elderly, experts warned the high deductibles for hospital coverage and the premiums and copays for physician coverage would force beneficiaries to self-ration care. “Those can be fixed,” President Lyndon B. Johnson said, “once it sinks in that Medicare is here to stay.”
Unfortunately, the government never addressed the problem. Instead, it allowed the insurance industry (with a big assist from AARP, the nation’s largest senior citizen lobby) to sell supplemental plans to cover out-of-pocket costs. Eventually, nearly three quarters of seniors in the program purchased such coverage either directly or through their former employer’s retirement plan.
However, as rising health care costs drove supplemental plan premiums skyward, a growing share of seniors opted for insurer-run Medicare Advantage plans, which promised additional benefits and low or no upfront premiums. Today, over half of seniors are in insurer-run plans, which are highly profitable because the government pays insurers more than if those beneficiaries had remained in traditional Medicare.
Cost-conscious seniors opting into Medicare Advantage rarely consider the downsides until they get seriously ill. Those can include large copays for some drugs, tests, and procedures; narrow provider networks; delayed care through prior authorization; and outright denials, which even when medically rational (many aren’t, driven by algorithms rather than an objective review of need) are rarely explained to beneficiaries.
Many employers are deploying similar tactics to limit their exposure to rising health care costs for the 180 million lives they cover. In 2006, only 10% of workers with employer-provided insurance were enrolled in high-deductible plans. Today, that number is more than 40%.
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The individual plans sold on the exchanges fell into a similar trap. Nearly half the 16.1 million people who signed up for Obamacare plans in 2024 chose bronze plans because of their low upfront premiums. However, they only cover about 70% of total health care costs. Another 7 million bought silver plans (80% coverage). Both groups can get socked with big copays and deductibles when they require health care.
The net result is escalating out-of-pocket costs for millions of the insured; the emotional devastation of having to deal with huge bills when seriously ill; and escalating medical debt.
The “skin in the game” experiment has failed to achieve its primary raison d’être. Over the past two decades, it has not prevented health care spending from growing at the same rate or faster than the rest of the economy. For patients’ sake, it needs to stop.
Merrill Goozner is the former editor-in-chief and columnist at Modern Healthcare. He now publishes GoozNews.
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