Inferential Questions Research Articles

L1 and L2 reading comprehension in Dutch higher education students with and without dyslexia: effects of test type and linguistic skills

ABSTRACT Reading is one of the most important skills in higher education. Various students show problems with reading, which can lead to problems with text decoding, reading comprehension, or both. In this study, we investigate the L1 (Dutch) and L2 (English) reading comprehension performance of students with and without dyslexia. Participants were presented with two short texts, and their reading comprehension was tested via two different test types: (1) a free recall test (i.e. summary writing) and (2) a true/false test, composed of literal and inferential questions. In addition, the students were timed during text reading and tested on L1 and L2 language proficiency and print exposure, to study if and how these skills relate to their reading comprehension performance. In L1, both student groups performed equally well on literal true/false questions, but students with dyslexia performed poorer on inferential questions and summary writing. In L2, students with dyslexia performed worse on both types of true/false questions and the summary writing. Language proficiency was shown to play the most prominent role in reading comprehension skills of higher education students.

Questioning to Elicit Inferential Responses: A Study of Teacher-Child Linguistic Interactions During Block Play Activities in Chinese Kindergartens

ABSTRACT Research Findings: This study employed a mixed-methods approach to examine how kindergarten teachers in China stimulate literal and inferential responses during block play. The quantitative study analyzed interactions in 17 randomly sampled classrooms from Guangzhou, observing one teacher per classroom. Using visual recording and non-participatory observation, we found four key results from ANOVA analyses: (1) teachers produced more utterances (n= 4,629) than children (n= 1,798), with 61.55% being comments and 38.45% questions; (2) teachers predominantly asked literal questions (78.77%), mirrored by children’s responses (75.71% literal); (3) a significant alignment existed between teachers’ inferential questions and children’s responses, termed “responsive interaction”; and (4) degree-holding teachers asked more questions, fostering more interaction and child responses. The qualitative study examined four successful and three unsuccessful cases to understand how teachers’ questions either scaffolded or failed to scaffold children’s inferential responses. These findings suggest that teachers’ inferential questioning elicits more inferential responses, with degree-holding teachers facilitating more responsive interactions. Practice or Policy: Degree-holding teachers’ inferential questioning enhances children’s inferential responses, emphasizing the importance of teacher education in fostering responsive interactions.

Preschoolers’ knowledge acquisition from German educational media: The impact of a training program fostering media sign literacy

ABSTRACT Evidence shows that the first component of media literacy to develop in children is media sign literacy (MSL), an ability that focuses on the understanding and correct use of the signs and symbol systems that organize different media. Previous research has shown that MSL is a significant predictor of learning from media. In this study, we investigated the impact of a self-developed tablet-based MSL training on children’s knowledge acquisition from educational media. A sample of 144 preschoolers aged 4 to 5 years old either received the training (training group) or did not (control group). The training program was executed over a period of 4 weeks. After the program, all participants answered recognition and inference questions about an educational film and an audiobook to assess their knowledge acquisition. The children’s verbal and nonverbal intelligence (both before the training) and MSL (before and after the training) were also assessed. The results showed that the training successfully improved the children’s MSL. Further, the children in the training group had significantly higher recognition and inference scores for the film, along with significantly higher inference scores for the audiobook. Regression analyses also revealed that MSL baseline scores (pretraining) predicted knowledge acquisition from the film but not audiobook.

“Why is the bear sad?”: preparing preservice educators to support children’s inferencing during storybook reading

ABSTRACT The study investigated the effects of an instructional unit for preservice educators aimed at developing their knowledge about children’s inferencing skills and strategies to support these skills during storybook reading. Participants were randomly assigned to either an experimental group that received instruction on supporting inferencing (n = 13) or a comparison group (n = 12) that received instruction on print referencing, another means of supporting emergent literacy. For both groups, instruction involved a videorecorded presentation; independent reading, observation, and reflection; and discussion and practice of the targeted strategies with peers in a virtual environment. Participants’ knowledge about emergent literacy and inferencing was evaluated via a questionnaire and a role-play. While both groups increased their knowledge about emergent literacy from pretest to posttest, only the experimental group improved significantly on knowledge about inferencing and asked a greater number and proportion of inferential questions during the role-play, as well as more diverse questions. The implications for education and professional development for preservice educators are discussed.

Investigating Test-Taking Strategies in an English Reading Comprehension Test Used by Secondary High School Students

Reading is a crucial skill that Indonesian learners are expected to master, as outlined in the 2013 Curriculum. Students are required to develop comprehension skills to understand both explicit and implicit information in a text. To assess students’ knowledge, teachers often use tests. During these reading tests, students may employ strategies to aid their comprehension. This study investigates the strategies used by secondary high school students at MA Darul Ihsan Samarinda during an English reading comprehension test, along with the challenges they face when using these strategies. This study employs a qualitative design to examine the strategies utilized by six students: high score, middle score, and lowest score. The data, gathered through the think-aloud method and interviews, is analyzed using thematic analysis. The findings reveal that students employed 23 out of 31 strategies to answer factual questions and 19 strategies for inferential questions. Both the high score and middle score students predominantly used the strategy of reading the text for clues in both types of questions. Conversely, the lowest score student primarily relied on the strategy of rereading the question. Despite the differences, all students utilized a meaning-based text strategy to choose their answers. The middle score and lowest score students frequently faced challenges with time management and complex vocabulary. Additionally, using mixed-method research designs could provide deeper insights into test-taking strategies. This study focused on the strategies used by high school students during reading tests, while other research has examined test-taking strategies in high-stakes language tests

Adversarial Entity Graph Convolutional Networks for multi-hop inference question answering

Multi-hop reasoning is critical for natural language understanding but poses challenges for current models, requiring models capable of aggregating and reasoning across multiple sources of information. We propose an Adversarial Entity Graph Convolutional Network (AEGCN) to improve multi-hop inference performance. Unlike previous GNNs-based models, AEGCN places a greater emphasis on the construction of rich entity graph which focuses on identifying the related entities from support document and connecting these entities with innovative edge relationships. The entity graph built by AEGCN preserves both the semantic information and structure of the original text. Further, adversarial training is adopted to generate challenging embeddings for the entity graph, increasing the model’s robustness against the interference. The experiments evaluated on WIKIHOP and MEDHOP dataset indicate that AEGCN achieves 6.8 % and 7.7 % accuracy improvement over baseline model respectively, confirming the model’s advanced capability in multi-hop reasoning tasks.

Identification and estimation of causal effects in the presence of confounded principal strata.

Principal stratification has become a popular tool to address a broad class of causal inference questions, particularly in dealing with non-compliance and truncation by death problems. The causal effects within principal strata, which are determined by joint potential values of the intermediate variable, also known as the principal causal effects, are often of interest in these studies. The analysis of principal causal effects from observational studies mostly relies on the ignorability assumption of treatment assignment, which requires practitioners to accurately measure as many covariates as possible so that all potential sources of confounders are captured. However, in practice, collecting all potential confounding factors can be challenging and costly, rendering the ignorability assumption questionable. In this paper, we consider the identification and estimation of causal effects when treatment and principal stratification are confounded by unmeasured confounding. Specifically, we establish the nonparametric identification of principal causal effects using a pair of negative controls to mitigate unmeasured confounding, requiring they have no direct effect on the outcome variable. We also provide an estimation method for principal causal effects. Extensive simulations and a leukemia study are employed for illustration.

Promoting Inference Generation: Using Questioning and Strategy Instruction to Support Upper Elementary Students

AbstractMaking inferences is essential to comprehending text. Teachers can ask inferential questions, model their thinking, and teach specific strategies to support upper elementary students in making inferences while reading.

Cognitive demand in parent–child shared book reading and home language development among dual language learners in low-income immigrant families

Aims and objectives: The existing literature indicates that shared book reading is associated with benefits in children’s home language development. Parents play a significant role in shaping children’s language outcomes. The purpose of this study was to investigate the relations among the quality of parents’ shared book reading, children’s language output, receptive vocabulary, and expressive vocabulary outcomes in the home language of dual language learners (DLLs) to inform book reading practices that can support DLLs’ early home language development. Methodology: 36 Mexican American (43–67 months) and 31 Chinese American (38–70 months) DLLs and their mothers, who were home language dominant, were recruited from Head Start programs, which are federally funded programs that provide early childhood education to low-income children, in Northern California. Data and analysis: Utterances produced by the parents during parent–child shared book reading were coded for cognitive demand levels. DLLs’ home language expressive vocabulary and receptive vocabulary and information about parents’ demographics and parent–child shared reading frequency were collected. Correlations and hierarchical regression analyses were computed. Findings/conclusions: The findings demonstrated possible cultural differences in mothers’ book reading quality regarding cognitive demand. Although maternal statements and questions were not significantly associated with DLLs’ home language receptive and expressive vocabulary for both groups, asking yes/no, referential, and inferential questions were more likely to evoke more language output from DLLs. Originality: This study extends the literature on home language development of low-income DLLs during parent–child shared reading, as only a few studies have investigated the significance of shared reading in the home language. Significance/implications: The findings of this study yield important implications for parents’ shared reading practices with low-income DLLs. Using all levels of cognitively demanding speech and encouraging DLLs to talk during shared reading may benefit their home language development.

Anytime-valid off-policy Inference for Contextual Bandits

Contextual bandit algorithms are ubiquitous tools for active sequential experimentation in healthcare and the tech industry. They involve online learning algorithms that adaptively learn policies over time to map observed contexts X t to actions A t in an attempt to maximize stochastic rewards R t . This adaptivity raises interesting but hard statistical inference questions, especially counterfactual ones: for example, it is often of interest to estimate the properties of a hypothetical policy that is different from the logging policy that was used to collect the data—a problem known as “off-policy evaluation” (OPE). Using modern martingale techniques, we present a comprehensive framework for OPE inference that relaxes unnecessary conditions made in some past works (such as performing inference at prespecified sample sizes, uniformly bounded importance weights, constant logging policies, and constant policy values, among others), significantly improving on them both theoretically and empirically. Importantly, our methods can be employed while the original experiment is still running (that is, not necessarily post hoc), when the logging policy may be itself changing (due to learning), and even if the context distributions are a highly dependent time series (such as if they are drifting over time). More concretely, we derive confidence sequences for various functionals of interest in OPE. These include doubly robust ones for time-varying off-policy mean reward values, but also confidence bands for the entire cumulative distribution function of the off-policy reward distribution. All of our methods (a) are valid at arbitrary stopping times; (b) only make nonparametric assumptions; (c) do not require importance weights to be uniformly bounded, and if they are, we do not need to know these bounds; and (d) adapt to the empirical variance of our estimators. In summary, our methods enable anytime-valid off-policy inference using adaptively collected contextual bandit data.

교수를 통한 학습(learning-by-teaching)이 메타인지적 판단에 미치는 영향

The present study investigated the effect of learning-by-teaching on learners' metacognitive judgments and examined how beneficial effects of teaching changed depending on whether or not retrieval practice was evoked during teaching. Undergraduate students studied science passages through two learning phases. In the first phase participants read the passage and in the second phase they either restudied them (control), created a video lecture with their notes (teaching without retrieval practice; TnRP), or taught without any materials (teaching with retrieval practice; TRP). Following each phase, they made metacognitive judgments regarding their comprehension of the passages, and then took a final test, which included retention and inference questions. The results showed that, despite the non-significant difference in the final test performance, there were significant differences in the judgments of learning (JOLs) across the three conditions. After the second learning phase, JOLs increased in the control group, remained unchanged in the TnRP group, and decreased in the TRP group, compared to their initial JOLs. As a result, the TRP group showed significantly lower levels of metacognitive bias and higher metacomprehension accuracy compared to the other groups. Our findings suggest that learning-by-teaching can mitigate overconfidence in learners and enhance metacomprehension accuracy, particularly when teaching is accompanied by retrieval practice.

The continued influence of implied and explicitly stated misinformation with different emotional valence

ABSTRACT Misinformation often affects people’s cognition and judgment even when they are aware of a retraction; this is known as the continued influence effect of misinformation (CIE). The aim of the present study was to verify if the expressive style affects the CIE with different emotional valence. We designed a 2 (emotional valence: positive or negative) * 2(expressive style: explicit or implied) * 2 (correction condition: uncorrected or corrected) within-subject experiment. Participants were asked to finish memory questions and inference questions. The results found an interaction between expressive style and emotional valence. For positive misinformation, the expressive style had no effect; for negative misinformation, the CIE was stronger in the explicitly stated condition compared with the implied condition. The explicitly stated misinformation was more difficult to correct than the implied misinformation. The results provided evidence to explain the role of emotional valence and expressive style on CIE.

The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies

Abstract A new and rapidly growing econometric literature is making advances in the problem of using machine learning methods for causal inference questions. Yet, the empirical economics literature has not started to fully exploit the strengths of these modern methods. We revisit influential empirical studies with causal machine learning methods aiming to connect the econometric theory on these methods with empirical economics. We focus on the double machine learning, causal forest and generic machine learning methods, in the context of both average and heterogeneous treatment effects. We illustrate the implementation of these methods in a variety of settings and highlight the relevance and value added relative to traditional methods used in the original studies.

Patients with chronic bipolar disorder show impairments in interpreting literary fiction - A preliminary explorative study with the short story task

IntroductionTheory of mind (ToM) is a crucial skill in navigating and functioning in the social world. Significant ToM impairment was consistently found in bipolar disorder; it can be both a state and trait marker of the disorder. However, most of the ToM tests are not sensitive enough to detect subtle individual differences, which would be necessary for an individualized treatment plan. The Short Story Task (SST) is a new way to sensitively assess individual differences in ToM performance. The aim of the study was to test the feasibility of SST in patients with bipolar disorder. Method31 persons (11 male, 20 female) with bipolar I disorder and 31 healthy individuals (15 males and 16 females) as a control group were recruited. SST was used to evaluate ToM performance. The SST uses a Hemingway novel, in which the patient is presented with a realistic social situation, where the motivations of the characters and the underlying relationships of events are not explicitly described. ResultsIn the explicit mental state reasoning questions the CG (M = 8.06) had significantly higher (p < 0.001) scores than the persons with bipolar I disorder (M = 5.03). There was no ceiling effect for explicit ToM scores in either group. Participants in CG (M = 8.03) also significantly outperformed (p = 0.006) the BG participants (M = 6.55) in the comprehension questions. The spontaneous mental state inference question was performed equally (M = 0.23) in both groups. Group assignment (t = −3.503, p < 0.001), comprehension score (t = 2.864, p = 0.006), and spontaneous mentalization (t = 2.846, p = 0.006) significantly predicted the explicit ToM performance. ConclusionsOverall, we found that the Short Story Task is a promising tool for measuring ToM in patients with bipolar disorder without ceiling effect. Primarily explicit ToM was found to be deficient, which corresponds well with the ToM literature in bipolar disorder. Contrary to our hypothesis we could not detect impairment in spontaneous ToM and found that patients living with bipolar disorder also showed deficits in comprehension. The lack of assessment of neurocognitive skills is a significant limitation of the current study.

Exploiting a natural experiment in assessing student learning outcomes in public and nonprofit administration: A demonstration

This study exploits a natural experiment to establish the equivalence and/or difference in student learning outcomes between online and face-to-face public and nonprofit administration courses. Its main contribution is thus methodological. We will reexamine the online v. classroom debate - the face-to-face lecture is still the most effective method to deliver course content to students - using a new dataset and estimation approach. Our research addresses this causal inference question: Does the format of course delivery impact student learning? The most robust empirical strategy to rule out alternative explanations in causal studies is the experimental approach. In this study, we did not employ the experimental research design or any standard techniques, for example, regression analysis, available to the program evaluator or policy analyst. Instead, we exploited a naturally occurring phenomenon in a classroom environment to approximate statistical equivalence in the characteristics of students in the online and classroom formats and satisfy the exogeneity assumption of the treatment variable. Its more practical contribution is the use of learning theory and new research in online pedagogy to discuss the study’s conclusions and implications for online programming, instruction, and program coordination. We developed the feedback as teaching philosophy or approach to close the gap between the learning outcomes of completely asynchronous online and entirely face-to-face classes in public administration.

Parent inferential questions and child responses during shared reading predict DLLs’ receptive vocabulary development

The present study investigated whether parent–child interactions during shared reading in a diverse sample of 91 three- to five-year-old dual language learners (DLLs) in Norway supported the development of the children’s first (L1) and second language (L2) vocabulary skills. The dyads spoke 11 different L1s (i.e., Arabic, Polish, Somali, and Urdu). The analysis of shared reading was based on predefined story elements in the book, and differentiated between whether central story elements were introduced as parent inferential questions and child responses, or as parent and child narrative statements. The children’s L1 and L2 receptive vocabulary skills were assessed prior to the reading as well as 8 months later. Regression analyses revealed that parent inferential questions predicted children’s L1 vocabulary development across the time-period. Child responses to adult inferential questions predicted their L1 and L2 vocabulary development. These findings support the growing research base that emphasizes the importance of eliciting child reasoning during shared reading.

Maps: A Statistical View

Maps provide a data framework for the statistical analysis of georeferenced data observations. Since the middle of the twentieth century, the field of spatial statistics has evolved to address key inferential questions relating to spatially defined data, yet many central statistical properties do not translate to spatially indexed and spatially correlated data, and the development of statistical inference for mapped data remains an active area of research. Rather than review statistical techniques, we review the different ways the maps of georeferenced data can influence statistical analysis, focusing especially on maps as data visualization, maps as data structures, and maps as statistics themselves, i.e., summaries of underlying patterns with accompanying uncertainty. The categories provide connections to disparate literatures addressing spatial analysis including data visualization, cartography, spatial statistics, and geography. We find maps integrate spatial analysis from motivating questions, informing analytic methods, and providing context for results.

Effectiveness Of Digital Text in Interactive Library for English Starters (Digi-Tiles in Enhancing The Reading Comprehension Of Grade 3 Learners)

This quasi-experimental study was conducted to measure the effectiveness of Digital Text in Interactive Library for English Starters (Digi-TILES), which is a digital learning tool, in improving the reading comprehension level of Grade 3 pupils of Sampiruhan Elementary School. The intervention tool was designed to develop reading comprehension of learners and results were measured through comparing pretest and post-test scores.Two (2) paired groups of learners participated in the study composed of 30 members for each group. Each group’s reading comprehension level was determined through standardized Phil-IRI materials. The digital tool intervention developed by the researcher was utilized by the experimental group while the comparison group utilized plain printed Pivot Self Learning Modules distributed by Department of Education. Paired and Independent Samples T-tests were used to answer the inferential questions of the study.The experimental group, after utilizing the intervention tool scored higher in their post-test than the comparison group. Comparing also the pretest and post-test of the experimental group, it is notable that the scores in the latter are significantly higher. This implies that the Digi-TILES learning tool has impacted the learners’ reading comprehension positively.The hypotheses stating that there is no significant difference between the post-test of the two groups were rejected. The findings therefore reflect that the experimental group scored higher those in the comparison group. The Digi-TILES therefore helped in enhancing the reading comprehension of Grade 3 learners. It is therefore suggested that the school may consider utilizing this tool to improve the quality of learners when it comes to reading comprehension.

Students Difficulties in Reading Comprehension on Descriptive Text at Second Semester FKIP in Batanghari University Jambi

The aim of this research is to identify the educational abilities and achievements of the students. challenges or hardships In the field of reading comprehension, Paraphrased: regarding texts that provide detailed descriptions. This kind of investigation is Quantitative data refers to information that can be measured and expressed numerically. The population sizes The focus of this research investigation is on this particular study. According to the research, the difficulty level of answering main idea questions is determined to be 0.3%. The difficulty for students in answering inference questions is 0.14. Additionally, the percentage of challenging questions that involve intricate information is 0.12% out of four total question items. The rate of students who have trouble comprehending vocabulary is 0.14%. The overall average score for the exam is 84.6. Students therefore fulfill the requirements for achieving a high score. Researchers have arrived at the inference that students possess a strong comprehension of descriptive material.

Eryn: a multipurpose sampler for Bayesian inference

ABSTRACTIn recent years, methods for Bayesian inference have been widely used in many different problems in physics where detection and characterization are necessary. Data analysis in gravitational-wave astronomy is a prime example of such a case. Bayesian inference has been very successful because this technique provides a representation of the parameters as a posterior probability distribution, with uncertainties informed by the precision of the experimental measurements. During the last couple of decades, many specific advances have been proposed and employed in order to solve a large variety of different problems. In this work, we present a Markov Chain Monte Carlo (MCMC) algorithm that integrates many of those concepts into a single MCMC package. For this purpose, we have built Eryn, a user-friendly and multipurpose toolbox for Bayesian inference, which can be utilized for solving parameter estimation and model selection problems, ranging from simple inference questions, to those with large-scale model variation requiring trans-dimensional MCMC methods, like the Laser Interferometer Space Antenna Global Fit problem. In this paper, we describe this sampler package and illustrate its capabilities on a variety of use cases.