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Homework and Achievement: Using Smartpen Technology to Find the Connection

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Abstract

There is a long history of research efforts aimed at understanding the relationship between homework activity and academic achievement. While some self-report inventories involving homework activity have been useful for predicting academic performance, self-reported measures may be limited or even problematic. Here, we employ a novel method for accurately measuring students’ homework activity using smartpen technology. Three cohorts of engineering students in an undergraduate statics course used smartpens to complete their homework problems, thus producing records of their work in the form of timestamped digitized pen strokes. Consistent with the time-on-task hypothesis, there was a strong and consistent positive correlation between course grade and time doing homework as measured by smartpen technology (r = .44), but not between course grade and self-reported time doing homework (r = −.16). Consistent with an updated version of the time-on-task hypothesis, there was a strong correlation between measures of the quality of time spent on homework problems (such as the proportion of ink produced for homework within 24 hr of the deadline) and course grade (r = −.32), and between writing activity (such as the total number of pen strokes on homework) and course grade (r = .49). Overall, smartpen technology allowed a fine-grained test of the idea that productive use of homework time is related to course grade.

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... Therefore, this data focus our attention on the type of homework activities and their effectiveness. Rawson et al. (2017) investigate the relationship between homework activity and academic achievement; they use a novel method for accurately measuring students' homework activity using smartpen technology. In this investigation three groups of engineering students in an undergraduate statistics course used smartpens to complete their homework problems, thus producing records of their work in the form of timestamped digitized pen strokes. ...
... Throughout the bibliographic review we could see different studies developed in order to answer several questions related to the relationship between homework and academic achievement Jerrim et al., 2020;Kalenkoski & Pabilonia, 2017); the importance of the cultural and economic differences between the students' families and their influence in completing homework (Alonso et al., 2016;Doctoroff & Arnold, 2017;Mora & Escardíbul, 2018;Polo & Bailén, 2016;Suárez et al., 2016;Valle et al., 2015;Weerasinghe, 2020;Xu et al., 2016Xu et al., , 2017; the type of activities developed through homework (Rawson et al., 2017;Zurcher et al., 2016); the teachers' perceptions about the effectiveness of homework (Snead & Burris, 2016); the importance of the teaching pedagogy and preparation about it (Bednarz & Proulx, 2017;Noyes, 2012); and studies where authors investigate the importance of mastery orientation (Du et al., 2016). ...
... Another aspect this research highlights is the family involvement when students are completing their homework, and we could see that this data determine the number of activities that students make per week, which determine, too, the time dedicated to performing the homework activities. And although other investigations (Rawson et al., 2017;Zurcher et al., 2016) show us conclusions about the type of activities developed in homework, in our case, we focus on the number of activities, which can be considered another point about the teacher decisions, and the planning and the coordination between the group of teachers. ...
Article
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The aim of this article is to define factors that influence elementary school teachers when assigning homework to students. The study was conducted with a sample of 93 teachers of the Region of Murcia (Spain). Teachers answered a questionnaire, with 35 questions, that was validated through an expert judgment by the Angoff method (Ricker, 2006). The analysis of data defined the factors, and they were: Family Involvement, Personal Feelings, Staff Decisions and Assumptions. Besides, the variables defined as frequency of homework, activities per week and needed to do homework, help us to conclude that there were decisive factors for teachers when giving homework to their students, and these factors were conditioned by the number of days per week that teachers assign tasks to their students, the estimated number of activities per week given to students, and the time needed to complete the tasks.
... Homework completion and the timeliness of the submission are associated with academic achievement (Bempechat, 2004;Calderwood et al., 2014;Cooper, 1989;Planchard et al., 2015;Rawson et al., 2017). Although providing students with practice opportunities through homework can promote learning achievement, simply assigning homework does not lead to successful homework completion (Planchard et al., 2015). ...
... Some students may not have sufficient motivation, interests, and sustained efforts to work on homework (Patall et al., 2010). Others procrastinate by working on it at the last minute, engendering a low quality of work (Rawson et al., 2017). Additionally, the challenges of completing homework timely rest on meeting deadlines and preventing procrastination (Corno, 1996;Xu, 2010Xu, , 2013. ...
... However, simply assigning homework does not naturally engender a better learning achievement due to a lack of interest or motivation (Amzalag, 2021;Patall et al., 2010;Planchard et al., 2015). Some students do not start their homework until there are less than 24 hours before it is due, potentially jeopardizing the quality of homework and final grade (Rawson et al., 2017). Additionally, students may not carry out effective strategies to help them persist and combat the challenges in completing homework. ...
Article
Homework completion is associated with learning achievement, but students’ challenges revolve around meeting deadlines and preventing procrastination. Promoting students’ self-regulated learning (SRL) can overcome these challenges. We explored the role of SRL (forethought and learning strategies) on the timeliness of homework submissions performed by undergraduate female students in quantitative courses. Data were collected from a survey and Learning Management System log. A structural equation modeling analysis indicated that the forethought components had a direct effect on the learning strategies. The use of learning strategies significantly influenced homework timeliness, which then significantly affected course achievement. Discussion regarding mixed results and strategies for scaffolding SRL through homework assignments are included. A suggestion on guiding students to execute suitable learning strategies for mastering quantitative topics is presented.
... A digital pen allows digitalizing all notes written with the pen on normal paper (more information in section Methodology). The positive effects of digital pen technology were already investigated in learning (Boyle & Joyce, 2019) and as a method to detect conducted homework (Rawson, Stahovich, & Mayer, 2017). Within the study by Rawson et al., a digital pen was used to automatically and reliably record the homework activity to find a connection with academic achievement (Rawson et al., 2017). ...
... The positive effects of digital pen technology were already investigated in learning (Boyle & Joyce, 2019) and as a method to detect conducted homework (Rawson, Stahovich, & Mayer, 2017). Within the study by Rawson et al., a digital pen was used to automatically and reliably record the homework activity to find a connection with academic achievement (Rawson et al., 2017). Further concepts describe the possibilities of digital pen usage as an intuitive assistance tool for persons with dementia to improve communication, for example, when writing or answering emails (Prange, Sandrala, Weber, & Sonntag, 2015). ...
... The possible advantages of digitalized cognitive assessments (Cernich et al., 2007;Sternin et al., 2019) and the advantages of a digital pen (Boyle & Joyce, 2019;Rawson et al., 2017) have already been examined in various studies. The use of a digital pen in cognitive testing can help to take advantage of digital cognitive assessments without the disadvantages of unfamiliarity or lack of acceptance. ...
Article
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Many digitalized cognitive assessments exist to increase reliability, standardization, and objectivity. Particularly in older adults, the performance of digitized cognitive assessments can lead to poorer test results if they are unfamiliar with the computer, mouse, keyboard, or touch screen. In a cross-over design study, 40 older adults (age M = 74.4 ± 4.1 years) conducted the Trail Making Test A and B with a digital pen (digital pen tests, DPT) and a regular pencil (pencil tests, PT) to identify differences in performance. Furthermore, the tests conducted with a digital pen were analyzed manually (manual results, MR) and electronically (electronic results, ER) by an automized system algorithm to determine the possibilities of digital pen evaluation. ICC(2,k) showed a good level of agreement for TMT A (ICC(2,k) = 0.668) and TMT B (ICC(2,k) = 0.734) between PT and DPT. When comparing MR and ER, ICC(2,k) showed an excellent level of agreement in TMT A (ICC(2,k) = 0.999) and TMT B (ICC(2,k) = 0.994). The frequency of pen lifting correlates significantly with the execution time in TMT A (r = 0.372, p = 0.030) and TMT B (r = 0.567, p < 0.001). A digital pen can be used to perform the Trail Making Test, as it has been shown that there is no difference in the results due to the type of pen used. With a digital pen, the advantages of digitized testing can be used without having to accept the disadvantages.
... The amount of time spent on assignments is another indicator that impacts students' academic achievement. Prior research shows that the more time students invest on assignments, the higher their academic achievement Rawson, Stahovich, & Mayer 2016). However, some studies claim that taking a longer time to do assignments did not improve academic achievement (Trautwein 2007;Trautwein et al. 2009). ...
... Assignments are an essential tool not only for high school students but also for college students (Kitsantas & Zimmerman 2009;Rawson et al. 2016). Assignments in universities refer to coursework either inside or outside the classroom in many kinds of assignments, including projects, presentations, discussion, and writing. ...
... Traditionally, higher education students in the US are assigned homework each day (Snyder 1998) but more importantly, assignments are included in the total grade calculation along with class participation and examination scores (International Student n.d.; Olson 2016). Therefore, students need to allocate their time to finish assignments (Rawson et al. 2016). Compared with secondary school teachers, college instructors do not follow or check assignment completion, and for this reason, college students need to take more responsibility (Kitsantas & Zimmerman 2009). ...
... In the present study, we extend the concept of metacognitive awareness to include the learner's judgment of the amount of time spent with instructional materials, which we call judgment of study time (JOST). Study time (or time on task) is considered one of the most important factors affecting student learning and academic success (Rawson et al. 2017;van Gog 2013). Therefore, in this study we examine the proposal that skill in judging one's study time could be an important metacognitive skill, supporting academic success. ...
... It stands to reason that if easily recalled achievement indicators are often misreported, measures that require more careful estimation and recall, such as selfreported study time, may be even more prone to error. In a recent study, researchers surveyed three cohorts of students about how much time they typically spent completing homework assignments during the term for a college engineering course, and used digital smartpens to accurately measure the actual time spent (Rawson et al. 2017). The study revealed only a weak correlation between self-reported time and actual time (r's range from .16 to .35). ...
... Previous research has shown little correlation between learning outcomes and self-reported time spent in various study activities, such as reading the textbook (Daniel and Woody 2013;Podolefsky and Finkelstein 2006), completing homework assignments (Rawson et al. 2017), or all study activities combined (Schuman et al. 1985). Schuman and colleagues found that neither self-reported study time nor time-use journals were significantly correlated to students' test scores, overall grade in the course, or GPA (Schuman et al. 1985). ...
Article
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The present work examines the accuracy of self-reports of study time for college students. In a 10-week Mechanical Engineering course, 99 college students accessed their textbook, homework solutions, graded work, and lecture slides via custom software that recorded objective measures of reading time. In addition, the students provided subjective judgments of the time they spent reading these materials. Comparisons between the objective and subjective measures reveal that students significantly overestimated time with the textbook, homework solutions, graded work, and lecture slides, with higher performing students overestimating to a lesser degree. The difference between objective and subjective judgments of study time correlated significantly and negatively with final course grade for the textbook (r = −.31), homework solutions (r = −.39), and lecture slides (r = −.24), but not for graded work (r = −.05). This study calls into question the utility of self-report data in studies of student study habits, and showcases the value of objective technology-based measures of such habits.
... The results of the current study denoting the benefits of increasing time committed to class level upgrading are supported in the literature, though not directly in terms of the power engineering discipline. Time committed to homework and study extended the time to learn outside the classroom and enhanced academic learning possibly through priming the active cognitive processing and learning functions (Cooper, 1989(Cooper, , 2001Mayer, 2011;Rawson, Stahovich, & Mayer, 2016). Engagement is the amount of time that a student commits to a task, and operates as a mechanism to influence learning outcomes, as indicated by achievement (Rawson et al., 2016). ...
... Time committed to homework and study extended the time to learn outside the classroom and enhanced academic learning possibly through priming the active cognitive processing and learning functions (Cooper, 1989(Cooper, , 2001Mayer, 2011;Rawson, Stahovich, & Mayer, 2016). Engagement is the amount of time that a student commits to a task, and operates as a mechanism to influence learning outcomes, as indicated by achievement (Rawson et al., 2016). ...
Article
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Power engineering certification in Canada comprises a hierarchical, graduated system available to both young and adult learners. This paper offers insight into the knowledge gap regarding factors influencing Canadian power engineers' decision to pursue advanced certification in the Provinces of British Columbia and Alberta, with implications for adult learning in the power engineering sector of Canada. Comprehension of factors that influence intentions for power engineering certification may illuminate barriers and enablers to adult learning and provide evidentiary knowledge to support a format that facilitates advancement of certification. The research methodology was quantitative correlational design in which linear and logistic regressions employing a modified Bonferroni equivalent alpha were utilised. An original survey was developed for the study and pilot tested for validity and reliability. The sample comprised 1st, 2nd, and 3rd Class power engineers in British Columbia and Alberta. The dependent variable (DV) was the power engineers' advancement intention. 198 Clayton Mullen and Yohannes Mariam In the context of this paper, advancement intention is an influence leading to the inclination or reluctance to pursue promotion, succession, or advancement in employment. The independent variables (IVs) were time commitment, educational support, locus of control, time elapsed since previous certification, responsibility, and peer appraisal. Revealed in the results were positive, statistically significant relationships between the DV of advancement intention and three of the six IVs. Time commitment, responsibility, and elapsed time exert statistically significant effects on advancement intention (DV). The three remaining IVs that did not exhibit significant relationships with the DV were educational support, locus of control, and peer appraisal. This indicated that the IVs of educational support, locus of control, and peer appraisal did not significantly influence the DV when compared to the significant influences of time commitment, responsibility, and elapsed time on the DV. Comprehension of the influential factors regarding the intention of Canadian power engineers to pursue advanced certification may assist industry and academia with insight into the barriers and enablers to higher certification, and the correlation of decision factors with advancement intention.
... The validity of subjective measures has been questioned however, as the administration of a questionnaire during a lesson may impact cognitive processes while recall bias may impact results if the questionnaire is administered after the lesson [46,47]. In contrast, objective measures such as eye-tracking [48], Web-log data [31,35,49], dual-task paradigms [50], brain activity [51], and biometrics like heart rate variability [52] are increasingly popular in extant literature and offer the advantage of directly tapping into the learner's behaviour during learning. By combining outcomes, this longitudinal analysis adds to the robustness of the research concerning the impact of the segmenting, signalling and embodiment principles on the cognitive processes of learning. ...
Article
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The purpose of this transaction log analysis was to evaluate university students’ engagement behaviours with a catalogue of multimedia lectures. These lectures incorporated selected instructional design principles from the cognitive theory of multimedia learning (CTML). Specifically, thirty-two multimedia lectures which differentially employed the signalling, segmenting and embodiment principles from the CTML were delivered to a cohort of 92 students throughout an academic trimester. Engagement with each multimedia lecture was measured in three domains: affective engagement was measured using a Likert-style survey that accompanied each multimedia lecture; behavioural engagement was measured using the web logs provided by YouTube Studio analytics (average watch time); cognitive engagement was measured using students’ average score on a quiz that accompanied each multimedia lecture. Separate multiple linear regression analyses for measures of affective, behavioural and cognitive engagement revealed that multimedia lectures that ‘stacked’ the instructional design principles of embodiment (whereby the lecture was interspersed with clips of an enthusiastic onscreen instructor), segmenting (where lectures were divided into shorter, user-paced segments) and signalling (where onscreen labels highlighted important material) increased measures of engagement, including overall watch time, number of survey submission and number of quiz attempts ( P < 0.05). There was no association between any of the tested principles and students’ quiz scores or their responses on the Likert-style survey. This study adds to the available literature demonstrating the effectiveness of the signalling, segmenting and embodiment principles for increasing learner engagement with multimedia lectures.
... An example of supportive interaction is the usage of the pen as assistive technology: Students with learning disabilities can benefit from digital pens providing an audio function [23]. Rawson et al. [29] investigated the use of the digital pen as monitoring device. They tracked university student's homework activity using digital pen technology and found the productive use of homework time to be related to the course grade. ...
... When a decision was made to extend the school day, homework was officially integrated in the school curriculum. In other words, at present homework is not necessarily completed at home (Rawson, Stahovich, & Mayer, 2017). Some students relate that homework helps them better understand the material taught in class and serves for them as a type of review that summarizes that which was learnt previously (Zu & Yuan, 2003). ...
Article
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The current study seeks to examine the perception of the three main populations that have a part in the educational and pedagogic domain: teachers, parents, and elementary school students, while comparing between religious and secular schools. The major hypothesis of the study is that teachers, parents, and students do not have congruent views on the aims and effectiveness of homework. Another hypothesis was that differences would be found between parents’ views of homework by religiosity. In addition, a negative association will be found between the teacher’s years on the job and attitude towards homework assignment–such that the more years of experience the more negative their attitudes towards homework assignment. Finally, differences will be found in the respondents’ views on homework assignment by the school’s geographic location. The research findings show that the first hypothesis was partially confirmed. Teachers are the most positive about homework, followed by students and finally parents. The confirmation was only partial, as the hypothesis was that students’ views would be the least supportive. The second hypothesis was not confirmed, as no significant differences were found between the views of religious and secular parents on homework. The findings concerning the third hypothesis found a significant negative correlation; such that the more experienced the teacher the more negative his or her attitude to homework, confirming the hypothesis. The conclusions of this study indicate that the homework format is in dispute and there is no consensus on this topic. It appears, at times, that it may be customary to act by force of habit in formal education, as in other areas. Therefore, it is necessary to conduct further research on the subject and to explore whether there is a need for change in the educational world, following the many changes that society has undergone over the years.
... [Veenman et al. 2003], por exemplo, criticam o modelo de detecçdetecç˜detecção dos EAs baseado em questionários, alegando que o estudante não está apto a dizer verdadeiramente o que lhé e melhor. [Rawson et al. 2017] ressalta ainda a baixa correlaçcorrelaç˜correlação entre o qué e respondido em um formulário e a verdade, evidenciando a baixa credibilidade dessas respostas. [Kirschner et al. 2013] apontam que a muitos dos EAs são determinísticos, onde ao aluno nãó e atribuído um EA baseado em um conjunto de pontuaçpontuaç˜pontuações em diferentes dimensões, masémas´masé classificado em um grupo específico. ...
Conference Paper
Adaptive and Intelligent Educational Systems (AIES) aim to provide a personalized assistance to the student by detecting their Learning Style (LS). One of the most popular techniques used for this detection is Reinforcement Learning (RL). However, RL presents slow convergence rate in some cases. This work proposes the improvement of an AIES by using an adaptation of the Dynamic Scripting technique, considered faster than the RL. Experimental results showed that the proposed technique reduced the number of the learning problems by ≈ 54%, and ≈ 35%, respectively, to Static and Dynamic LS, while reduced the number of interactions by ≈ 5.7% in relation to the literature. Resumo. Sistemas Adaptativos e Inteligentes para a EducaçEducaç˜Educação (SAIE) buscam fornecer assistência personalizada ao aluno por meio da detecçdetecç˜detecção de seu Estilo de Aprendizagem (EA). Uma das técnicas mais exploradas para esta detecçdetecç˜detecçãó e o Aprendizado por Reforço (AR). Contudo, tal técnica, em alguns casos, ´ e con-siderada de lenta convergência. Este trabalho propõe o aperfeiçoamento de um SAIE utilizando uma adaptaçadaptaç˜adaptação da técnica de Dynamic Scripting, considerada mais rápida que o AR. Experimentos demostraram que a proposta, comparadàcomparadà as soluçsoluç˜soluções da literatura, reduziu o número de problemas de aprendizagem em ≈ 54%, e ≈ 35%, respectivamente, para EAs Estáticos e Dinâmicos, enquanto reduz o número de interaçinteraç˜interações do sistema em ≈ 5,7%.
... Conversely, a study found students overestimate their time doing homework when compared to a technology-based monitoring system. 27 In addition, there could be some social or norm-bias when reporting preparation time although one study found negligible contributions on large scale student reports of academic behaviors. 28 The recall limitation could be overcome with study logs but this method also would have its limitations. ...
Article
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Objective. To examine how instructor-developed reading material relates to pre-class time spent preparing for the readiness assurance process (RAP) in a team-based learning (TBL) course. Methods. Students within pharmacokinetics and physiology were asked to self-report the amount of time spent studying for the RAP. Correlation analysis and multilevel linear regression techniques were used to identify factors within the pre-class reading material that contribute to self-reported study time. Results. On average students spent 3.2 hours preparing for a section of material in the TBL format. The ratio of predicted reading time, based on reading speed and word count, and self-reported study time was greater than 1:3. Self-reported study time was positively correlated with word count, number of tables and figures, and overall page length. For predictors of self-reported study time, topic difficulty and number of figures were negative predictors whereas word count and number of self-assessments were positive predictors. Conclusion. Factors related to reading material are moderate predictors of self-reported student study time for an accountability assessment. A more significant finding is student self-reported study time is much greater than the time predicted by simple word count.
... La razón es que los estudiantes no son capaces o no están dispuestos a informar lo que en realidad hacen, o lo que creen que hacen. Para ilustrar la falta de fiabilidad del autoinforme, Rawson, Stahovich y Mayer [7] le preguntaron a un grupo de estudiantes cuándo hicieron su tarea y cuánto tiempo trabajaron en ella. Si bien hubo una significativa correlación positiva entre la cantidad de tiempo que los estudiantes pasaron trabajando en su tarea (medido por un "bolígrafo inteligente") y la nota obtenida por los estudiantes en el curso, no hubo correlación significativa entre la nota y el tiempo que los estudiantes dijeron haber dedicado a la tarea. ...
Article
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Es innegable el crecimiento de las investigaciones sobre estilos de aprendizaje, entre ellas se encuentra el artículo publicado recientemente en la Revista Cubana de Educación Médica Superior: "Experiencia en la adaptación de actividades a los estilos de aprendizaje desde la educación de posgrado a distancia". En este artículo, los autores definen los estilos de aprendizaje como "aquellos rasgos cognitivos, afectivos y fisiológicos que sirven como indicadores relativamente estables de cómo los estudiantes perciben, interaccionan y responden a sus ambientes de aprendizaje". De igual manera, otros investigadores relacionan los estilos de aprendizaje a las aptitudes del ser humano, su talento, medios, instrumentos personales con los que cuentan para interactuar con la realidad, de forma efectiva según su propia característica; lo cual, tiene un gran valor para los educadores y psicopedagogos en el importante objetivo de mejorar y personalizar el aprendizaje de sus estudiantes.
... Recently, Rawson, Stahovich, and Mayer (2016) presented a basic model of academic learning that considered the fact that "engagement (as indicated by the amount of time that students allocate to a task) is a mechanism affecting learning outcomes (as indicated by achievement)" (p. 2). ...
Article
Time on task has been recognized as an important variable in academic learning, but self‐report measures of study time are problematic. Therefore, this study employs an automated system for recording time spent reading a course textbook. College students in an introductory engineering course accessed their textbook online. The book contained pages of instructional text, worked examples, homework problems, and answers to homework problems. An instrumented document reader program called “STL Reader” recorded the time each student spent on each page, thus providing detailed measures of reading habits. Across the 10‐week course, students spent an average of 1.9 hr reading instructional text, 1.4 hr on worked examples, 22.1 hr on homework problems, and 0.9 hr on homework answers, indicating a preference for practicing to solve test problems (i.e., self‐testing) rather than being told (i.e., receiving direct instruction). Furthermore, course grade (based largely on solving problems on exams and quizzes) correlated significantly and positively with time viewing homework problems, but not with time viewing either instructional text or worked examples, indicating that achievement was related to time spent practicing for solving test problems but not to time spent being instructed. Results suggest a revision of the time‐on‐task hypothesis to include the value of spending time on tasks aligned to test requirements. Lay Description What is currently known?: • Time‐on‐task theory states that students' time engaged in relevant material is an important factor in learning and achievement. • How students choose to process presented information is important for academic learning. • Undergraduate STEM students often read very little of the assigned course textbook. What this paper adds: • Technology‐enhanced data collection provides more accurate measure of students' engagement with e‐textbook. • Time spent viewing homework problems is significantly and positively related to achievement in an undergraduate engineering course. • Student grades were not positively correlated with time spent viewing instructional text or worked examples from the textbook. Implications: • Suggests revision of time‐on‐task hypothesis to include the value of spending time on tasks aligned to test requirements.
... Critica este ponto, alegando que o estudante não está apto a dizer verdadeiramente o que lheé melhor. [Rawson et al. 2017] concordam com essa premissa e ainda ressaltam a baixa correlação entre o queé respondido em um formulário e a verdade, evidenciando a baixa credibilidade dessas respostas. ...
Article
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Adaptive and Intelligent Education Systems (SAIEs) seek to provide personalized student assistance by detecting their Learning Style (EA). One of the most exploited techniques for this detection is Reinforcement Learning (RA). However, such technique, in some cases, is considered of slow convergence. This work proposes the improvement of a SAIE using an adaptation of the technique of textit Dynamic Scripting, considered faster than the AR. Experiments using a student behavior simulator demonstrated that the approach, compared to the literature solutions, reduced the number of learning problems by ≈ 54 %, and ≈ 35 %, respectively, for Static and Dynamic EAs, while increasing the students' average score by ≈ 6.06 %, which are very promising results.
... However, when it was decided to prolong the school day, homework was officially incorporated into the school curriculum. Therefore, homework is not necessarily prepared at home (Rawson et al., 2017). Homework can be defined as learning activities and tasks that are meant to be performed after the formal school lessons, and which can be performed within the school area (the school library or classroom); but even using this expanded definition, the students are required to complete any unfinished tasks at home (Davidovitch & Yavich, 2017). ...
Article
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The purpose of this study is to investigate the attitudes of three influential groups toward homework: parents, teachers and the public. Specifically, attitudes toward reducing and eliminating homework, as well as creating alternatives to conventional homework, are examined. The first hypothesis is that the attitude of teachers and parents toward homework is positive, whereas that of the public is negative, in line with the Israeli Ministry of Education that suggests changing homework policies. Another hypothesis is that there is a correlation between the seniority of teachers and their attitudes toward homework—the greater the seniority, the more negative the attitude. Finally, the effect of various background variables of teachers (class grade and subject area: sciences or humanities) and parents (age of parents, number of children in the family and child birth-order) on their attitudes toward homework is examined. The first research hypothesis was confirmed—most teachers and parents are supportive of homework, whereas the public is the least supportive. In addition, the public is the most supportive of changing homework policies, parents are less supportive, and teachers are the least supportive. No correlation was found between the seniority of teachers and their attitudes toward homework. Finally, background variables have no effect on attitudes toward homework. According to these findings, the public is not yet ready to completely forgo homework, which has been so widely used and accepted. The desire to change the traditional homework policy exists, but the concept that homework is essential remains.
... The reason for this is that learners are either not able and/or not willing to truthfully report what they do or what they think that they do. To illustrate the unreliability of self-report, Rawson, Stahovich, and Mayer (2016) asked students when they did their homework and how long they worked on it. They also gave these students a 'smartpen' which noted when and how long they worked on their homework. ...
Article
We all differ from each other in a multitude of ways, and as such we also prefer many different things whether it is music, food or learning. Because of this, many students, parents, teachers, administrators and even researchers feel that it is intuitively correct to say that since different people prefer to learn visually, auditively, kinesthetically or whatever other way one can think of, we should also tailor teaching, learning situations and learning materials to those preferences. Is this a problem? The answer is a resounding: Yes! Broadly speaking, there are a number of major problems with the notion of learning styles. First, there is quite a difference between the way that someone prefers to learn and that which actually leads to effective and efficient learning. Second, a preference for how one studies is not a learning style. Most so-called learning styles are based on types; they classify people into distinct groups. The assumption that people cluster into distinct groups, however, receives very little support from objective studies. Finally, nearly all studies that report evidence for learning styles fail to satisfy just about all of the key criteria for scientific validity. This article delivers an evidence-informed plea to teachers, administrators and researchers to stop propagating the learning styles myth.
... Existem trabalhos críticos aos EAs, a exemplo [Kirschner and van Merriënboer 2013] afirmam que o EA é estático, o que faz com que o aluno seja classificado em um grupo específico. Por sua vez, [Rawson et al. 2017] evidenciam a baixa credibilidade dos métodos utilizados para detecção de EA. Entretanto, diversos outros estudos como [Bernard et al. 2017], [Carvalho et al. 2017] e [Kolekar et al. 2017] defendem a utilização de EAs e apresentam diferentes técnicas para sua identificação. ...
Conference Paper
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Recentemente, os Sistemas de Gerenciamento de Aprendizagem têm sido muito utilizados no apoio à Educação. No entanto, a maioria destes sistemas fornece o mesmo conteúdo de iguais maneiras e formatos a todos os alunos, o que não favorece o processo de ensino-aprendizagem. Neste sentido, este trabalho, baseando-se no modelo de Estilos de Aprendizagem de Felder e Silverman, apresenta uma abordagem automática para a detecção de Estilos de Aprendizagem de estudantes para que o conteúdo ofertado seja modelado de acordo com as preferências dos mesmos. O modelo proposto, fundamentado em técnicas de clusterização e classificação, mostrou-se promissor, alcançando acurácia de detecção de até 90%.
... For instance, even if instructional time were comparable in STEM subjects in G8 and G9, the overall instructional time per week in lower secondary school in G8 increased, which had an impact on the amount of time at home and students' leisure time (Milde-Busch et al., 2010;Hübner et al., 2017a). Time at home constitutes a quite important predictor for school performance, for instance because students' schoolrelated engagements with parents can contribute to their learning (Berkowitz et al., 2015), investing time in homework might improve student achievement (Rawson et al., 2017), and leisure time can be used for addressing specific learning gaps, preparing for exams outside from school, or to recover from school-related stress (Milde-Busch et al., 2010). Further, girls were found to invest more time at home for school-related purposes (Wagner et al., 2008), which might also explain potential differential effects of the G8-reform. ...
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... Over the years, several studies have been made to describe students' study habit using study habit inventories (Wrenn and Humber, 1941;Brown and Holtzman, 1955;Thompson 1976;Houston, 1987;Malik and Parveen, 2013;Goel, 2014;Numan and Hasan, 2017;Rawson et al. 2017). Effective study habits are conditions in which the students study habitually to reach the maximum success of their academic in the school work (Ogbodo, 2010). ...
... The timestamps were used to identify students who submitted assignments and quizzes at the last minute (within 12 hours or less of the submission deadline) versus those who submitted in advance (more than 24 hours in advance). Identifying the submission time is deemed important because waiting until within 24 hours of the due time may negatively influence the homework quality and course grades (Rawson et al., 2017). The final letter grade was chosen as a grouping variable to understand differences between grade groups A, B, C, D, and F. The details of our methodology are presented in the next section. ...
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... Beyond higher education, smartpens can be utilized as valuable accommodations for professionals in a variety of careers, e.g., medical [29] and real estate [7]. Research has also been conducted utilizing the smartpen as a data collection tool to monitor undergraduate student homework completion [28] and as instructional tools for professors [30]. The literature base is lacking a students' perspective on the impacts of the Livescribe smartpen as an accommodation in higher education, therefore providing the rationale for this study. ...
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... Interest promotes learning, as it emotionally connects students with the curriculum (Kahu et al., 2015), and also increases exploration and information-seeking behaviors (Krapp, 1999). In addition, interest has been reported to be positively correlated with time and effort dedicated to study (Schiefele, 1991) and, likewise, linked with academic performance (Rawson et al., 2017). Interaction with a 3D visualization on osmosis that evokes a positive emotional experience may lead to improved learning outcomes of this important concept. ...
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Background Mobile‐based assessment has been an active area of research in the field of mobile learning. Prior research has demonstrated that mobile‐based assessment systems positively affect student performance. However, it is still unclear why and how these systems positively affect student performance. Objectives This study aims to identify the determinants of student performance during students' use of a mobile‐based assessment application in a formative assessment activity as part of English as a Foreign Language courses in higher education. Methods A structural model based on hypotheses will be validated using partial least squares‐structural equation modelling with data from the interaction of around 100 students of English as a Foreign Language (EFL) courses from the A1 and A2 levels of English that used a mobile‐based assessment system for a period of 4 weeks. Results and Conclusions This registered report describes the related work, hypotheses development, methodology, and proposed analysis to validate the structural model based on hypotheses. No results or conclusions have been obtained yet.
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We live in a time of "epistemic uncertainty" (Kay & King 2020) and where home schooling and remote teaching as a consequence of COVID-19 has become a global phenomenon in recent months. By 19 March 2020, 102 countries globally had shut all schools, affecting almost 900 million children and youth (UNESCO 2020, OECD 2020). Further school shutdowns have continued from 19 March up to June 2020, involving more countries and regions and impacting over 60% of the world's student population (UNESCO 2020). And UNESCO "is supporting countries in their efforts to mitigate the immediate impact of school closures, particularly for more vulnerable and disadvantaged communities, and to facilitate the continuity of education for all through remote learning" (p. 1). Since WHO (2020) states that we will probably experience similar pandemics as COVID-19 in the future, there is reason to believe that home schooling and remote teaching will affect and partly change education in the years to come. We therefore need to build on the current state of knowledge, examining "how teachers teach and learners learn" in this extraordinary coronavirus situation as well as how this affects pupils' digital Bildung journey.
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One approach to encourage productive study strategies is to incorporate preparatory quizzes (or pre‐quizzes) in which students are required to submit answers to questions before the underlying material is covered in class. In the present study, students took an introductory mechanical engineering class that either included pre‐quizzes (treatment group) or did not (control group). Students in the treatment group visited the online textbook more often and earlier in advance of deadlines, indicating better management of their study time—behaviors that have been shown to be productive study strategies. They also performed better in the course, indicating that techniques intended to prime productive study strategies can pay off. Finally, measures of productive learning strategies correlated with measures of course performance for both groups. These findings support the pretesting principle, which holds that students study more effectively and learn better when they take practice tests before a lesson.
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Dr. Thomas Stahovich gave a keynote in the morning of the first day of the conference. He showed how researchers have long sought to understand the effects of study skills on academic achievement but found no consistent relationship between them. Dr. Stahovich explained that this is due, in part, to the use of research methods that rely on surveys and students’ self-reports of study habits. In his work, he overcomes this limitation by using smartpens and an instrumented document viewer to objectively measure studying. This combination of technology provides a fine-grained view of the learning process not available with conventional assessment methods and enables the use of data mining to examine the relationship between studying and achievement. In his talk he presented novel data mining techniques, as well as the results of several studies that reveal new insights about the relationship between traditional learning activities—completing homework, taking lecture notes, and reading—and performance in introductory engineering courses. Finally, Dr. Stahovich discussed interventions that are based on these insights and are designed to improve student engagement and increase academic achievement. This chapter provides an edited transcription of that keynote. Thank you to David Hoeft for videotaping the sessions.
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The aim of this paper is to survey recent research publications that use Soft Computing methods to answer education-related problems based on the analysis of educational data ‘mined’ mainly from interactive/e-learning systems. Such systems are known to generate and store large volumes of data that can be exploited to assess the learner, the system and the quality of the interaction between them. Educational Data Mining (EDM) and Learning Analytics (LA) are two distinct and yet closely related research areas that focus on this data aiming to address open education-related questions or issues. Besides ‘classic’ data analysis methods such as clustering, classification, identification or regression/analysis of variances, soft computing methods are often employed by EDM and LA researchers to achieve their various tasks. Their very nature as iterative optimization algorithms that avoid the exhaustive search of the solutions space and go for possibly suboptimal solutions yet at realistic time and effort, along with their heavy reliance on rich data sets for training, make soft computing methods ideal tools for the EDM or LA type of problems. Decision trees, random forests, artificial neural networks, fuzzy logic, support vector machines and genetic/evolutionary algorithms are a few examples of soft computing approaches that, given enough data, can successfully deal with uncertainty, qualitatively stated problems and incomplete, imprecise or even contradictory data sets – features that the field of education shares with all humanities/social sciences fields. The present review focuses, therefore, on recent EDM and LA research that employs at least one soft computing method, and aims to identify (i) the major education problems/issues addressed and, consequently, research goals/objectives set, (ii) the learning contexts/settings within which relevant research and educational interventions take place, (iii) the relation between classic and soft computing methods employed to solve specific problems/issues, and (iv) the means of dissemination (publication journals) of the relevant research results. Selection and analysis of a body of 300 journal publications reveals that top research questions in education today seeking answers through soft computing methods refer directly to the issue of quality – a critical issue given the currently dominant educational/pedagogical models that favor e-learning or computer- or technology-mediated learning contexts. Moreover, results identify the most frequently used methods and tools within EDM/LA research and, comparatively, within their soft computing subsets, along with the major journals relevant research is being published worldwide. Weaknesses and issues that need further attention in order to fully exploit the benefits of research results to improve both the learning experience and the learning outcomes are discussed in the conclusions.
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The purpose of this thesis is to examine which data captured by experiential learning technology can be used to classify students into learning theory-based categories in a manner that provides actionable insights to educators. The objective is to examine how technology-enabled classification of learners into learning theory-based categories can be used by learning facilitators and instructional designers to improve the practice of experiential learning in higher education institutions. The study adopts an anti-positivist perspective that acknowledges habit as a driver of deterministic behaviour and that deterministic behaviour can be examined using scientific methods. The data used in this research is retrospective de-identified student learning data captured by an experiential learning technology used to structure and support the facilitation of an experiential business project program. The research findings outline the quantitative outcomes followed by an integrative qualitative discussion that explores how the findings could be used to inform the practice of experiential learning design and facilitation. Specifically, the results section outlines the experiential business project program design, the classification of learning tasks and the results of student responses to three surveys: Revised Implicit Theories of Intelligence Survey, Revised Two Factor Study Process Questionnaire and a learning history survey. The results section concludes with an examination of the five multiple regression analyses conducted. The purpose of the examination is to explore the extent to which experientially learning technology could accurately classify students into learning theory based categories. The integrative discussion examines each of the three research questions explicitly. The discussion focused on research question one examines the nature of the learning tasks that have a significant relationship with one or more of the learning theory based categories. It investigates whether there is an alignment between what is known about the nature of learners who exhibit or employ a particular mindset, approach to learning or learning history and the learning task categories identified as having a significant relationship with them. The discussion focused on research question two examines what additional data could be captured to improve the multiple regression models. Furthermore, it presents a framework for classifying learning tasks and discusses specific steps to be taken to improve the predictive power of the regression models. The discussion focused on research question three examines how displaying real-time predictive classification of learner behaviour alongside learning theory insights could be used by instructional designers and learning facilitators. The discussion explores how facilitators and learning designers could use the information to customise facilitator support, aid in the development of incentives that encourage learners to engage with learning content that they do not naturally lean towards and support the adaption of learning content to align better with a learner's motives. This study further proposes an example of the benefits of integrating learning analytics and learning theory, how learning theory based categorisation could enable more use of experiential learning within higher education institutions, enable experiential learning facilitators to provide more tailored support of students during experiential learning programs and how the results of the analysis could help students extract more of the benefits from the available learning out of experiential learning programs.
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The relation between college grades and self-reported amount of effort was examined in four major and several minor investigations of undergraduates in a large state university. Grades were operationalized mainly by using grade point average (GPA), though in one investigation grades in a particular course were the focus. Effort was measured in several different ways, ranging from student estimates of typical study over the term to reports of study on specific days. Despite evidence that these self-reports provide meaningful estimates of actual studying, there is at best only a very small relation between amount of studying and grades, as compared to the considerably stronger and more monotonic relations between grades and both aptitude measures and self-reported class attendance. The plausible assumption that college grades reflect student effort to an important extent does not receive much support from these investigations. This raises a larger question about the extent to which rewards are linked to effort in other areas of life—a connection often assumed but seldom investigated.
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Many students are being left behind by an educational system that some people believe is in crisis. Improving educational outcomes will require efforts on many fronts, but a central premise of this monograph is that one part of a solution involves helping students to better regulate their learning through the use of effective learning techniques. Fortunately, cognitive and educational psychologists have been developing and evaluating easy-to-use learning techniques that could help students achieve their learning goals. In this monograph, we discuss 10 learning techniques in detail and offer recommendations about their relative utility. We selected techniques that were expected to be relatively easy to use and hence could be adopted by many students. Also, some techniques (e.g., highlighting and rereading) were selected because students report relying heavily on them, which makes it especially important to examine how well they work. The techniques include elaborative interrogation, self-explanation, summarization, highlighting (or underlining), the keyword mnemonic, imagery use for text learning, rereading, practice testing, distributed practice, and interleaved practice. To offer recommendations about the relative utility of these techniques, we evaluated whether their benefits generalize across four categories of variables: learning conditions, student characteristics, materials, and criterion tasks. Learning conditions include aspects of the learning environment in which the technique is implemented, such as whether a student studies alone or with a group. Student characteristics include variables such as age, ability, and level of prior knowledge. Materials vary from simple concepts to mathematical problems to complicated science texts. Criterion tasks include different outcome measures that are relevant to student achievement, such as those tapping memory, problem solving, and comprehension. We attempted to provide thorough reviews for each technique, so this monograph is rather lengthy. However, we also wrote the monograph in a modular fashion, so it is easy to use. In particular, each review is divided into the following sections: General description of the technique and why it should work How general are the effects of this technique? 2a. Learning conditions 2b. Student characteristics 2c. Materials 2d. Criterion tasks Effects in representative educational contexts Issues for implementation Overall assessment The review for each technique can be read independently of the others, and particular variables of interest can be easily compared across techniques. To foreshadow our final recommendations, the techniques vary widely with respect to their generalizability and promise for improving student learning. Practice testing and distributed practice received high utility assessments because they benefit learners of different ages and abilities and have been shown to boost students’ performance across many criterion tasks and even in educational contexts. Elaborative interrogation, self-explanation, and interleaved practice received moderate utility assessments. The benefits of these techniques do generalize across some variables, yet despite their promise, they fell short of a high utility assessment because the evidence for their efficacy is limited. For instance, elaborative interrogation and self-explanation have not been adequately evaluated in educational contexts, and the benefits of interleaving have just begun to be systematically explored, so the ultimate effectiveness of these techniques is currently unknown. Nevertheless, the techniques that received moderate-utility ratings show enough promise for us to recommend their use in appropriate situations, which we describe in detail within the review of each technique. Five techniques received a low utility assessment: summarization, highlighting, the keyword mnemonic, imagery use for text learning, and rereading. These techniques were rated as low utility for numerous reasons. Summarization and imagery use for text learning have been shown to help some students on some criterion tasks, yet the conditions under which these techniques produce benefits are limited, and much research is still needed to fully explore their overall effectiveness. The keyword mnemonic is difficult to implement in some contexts, and it appears to benefit students for a limited number of materials and for short retention intervals. Most students report rereading and highlighting, yet these techniques do not consistently boost students’ performance, so other techniques should be used in their place (e.g., practice testing instead of rereading). Our hope is that this monograph will foster improvements in student learning, not only by showcasing which learning techniques are likely to have the most generalizable effects but also by encouraging researchers to continue investigating the most promising techniques. Accordingly, in our closing remarks, we discuss some issues for how these techniques could be implemented by teachers and students, and we highlight directions for future research.
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Cluster analysis is a group of statistical methods that has great potential for analyzing the vast amounts of web server-log data to understand student learning from hyperlinked information resources. In this methodological paper we provide an introduction to cluster analysis for educational technology researchers and illustrate its use through two examples of mining click-stream server-log data that reflects student use of online learning environments. Cluster analysis can be used to help researchers develop profiles that are grounded in learner activity—like sequence for accessing tasks and information, or time spent engaged in a given activity or examining resources—during a learning session. The examples in this paper illustrate the use of a hierarchical clustering method (Ward’s clustering) and a non-hierarchical clustering method (k-Means clustering) to analyze characteristics of learning behavior while learners engage in a problem-solving activity in an online learning environment. A discussion of advantages and limitations of using cluster analysis as a data mining technique in educational technology research concludes the article.
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David C. Berliner's title paper is a research review which explores factors that can be controlled or influenced by teachers and that are known to affect student behavior, attitudes, and achievement. Pre-instructional factors include decisions about content, time allocation, pacing, grouping, and activity structures. "During-Instruction" factors include engaged time, time management, monitoring success rate, academic learning time, monitoring, structuring, and questioning. Communicating academic expectations for achievement; developing a safe, orderly, and academically focused environment; sensible management of deviancy; and developing cooperative learning environments are climate factors. Among post-instructional factors are tests, grades, and feedback. Jane H. Applegate's response to Berliner's paper agrees that educational research has many promising avenues but also states that the research results must be useful to teachers and in a form that they can use. Ken Henson's response also states that educators must keep abreast of research and that they must use such findings to improve education. (CJB)
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Cognitive load theory (CLT) has been successful in identifying instructional formats that are more effective and efficient than conventional problem solving in the initial, novice phase of skill acquisition. However, recent findings regarding the “expertise reversal effect” have begun to stimulate cognitive load theorists to broaden their horizon to the question of how instructional design should be altered as a learner's knowledge increases. To answer this question, it is important to understand how expertise is acquired and what fosters its development. Expert performance research, and, in particular, the theoretical framework of deliberate practice have given us a better understanding of the principles and activities that are essential in order to excel in a domain. This article explores how these activities and principles can be used to design instructional formats based on CLT for higher levels of skills mastery. The value of these formats for e-learning environments in which learning tasks can be adaptively selected on the basis of online assessments of the learner's level of expertise is discussed.
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This paper review the 10-year history of tutor development based on the ACT theory (Anderson, 1993, 1993). We developed production system models in ACT of how students solved problems in LISP geometry, and algebra. Computer tutors were developed around these cognitive models. Construction of these tutors was guided by a set of eight principles loosely based on the ACT theory. Early evaluations of these tutors usually but not always showed significant achievement gains. Best-case evaluations showed that students could achieve at least the same level of proficiency as conventional instruction in one third the time. Empirical studies showed that students were learning skills in production-rule units and that the best tutorial interaction style was one in which the tutor provides immediate feedback, consisting of short and directed error messages. The tutors appear to work better if they present themselves to students as nonhuman tools to assist learning rather than as emulations of human tutors. Students working with these tutors display transfer to other environments to the degree that they can map the tutor environment into the test environment. These experiences have coalesced into a new system for developing and deploying tutors. This system involves first selecting a problem-solving interface, then constructing a curriculum under the guidance of a domain expert, then designing a cognitive model for solving problems in that environment, then building instruction around the productions in that model, and finally deploying the tutor in the classroom. New tutors are being built in this system to achieve the NCTM standards for high school mathematics in an urban setting.
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Article
Cognitive Load Theory John Sweller, Paul Ayres, Slava Kalyuga Effective instructional design depends on the close study of human cognitive architecture—the processes and structures that allow people to acquire and use knowledge. Without this background, we might recognize that a teaching strategy is successful, but have no understanding as to why it works, or how it might be improved. Cognitive Load Theory offers a novel, evolutionary-based perspective on the cognitive architecture that informs instructional design. By conceptualizing biological evolution as an information processing system and relating it to human cognitive processes, cognitive load theory bypasses many core assumptions of traditional learning theories. Its focus on the aspects of human cognitive architecture that are relevant to learning and instruction (particularly regarding the functions of long-term and working memory) puts the emphasis on domain-specific rather than general learning, resulting in a clearer understanding of educational design and a basis for more effective instructional methods. Coverage includes: • The analogy between evolution by natural selection and human cognition. • Categories of cognitive load and their interactions in learning. • Strategies for measuring cognitive load. • Cognitive load effects and how they lead to educational innovation. • Instructional design principles resulting from cognitive load theory. Academics, researchers, instructional designers, cognitive and educational psychologists, and students of cognition and education, especially those concerned with education technology, will look to Cognitive Load Theory as a vital addition to their libraries.
Book
For hundreds of years verbal messages such as lectures and printed lessons have been the primary means of explaining ideas to learners. Although verbal learning offers a powerful tool, this book explores ways of going beyond the purely verbal. Recent advances in graphics technology have prompted new efforts to understand the potential of multimedia and multimedia learning as a means of promoting human understanding. In Multimedia Learning, Second Edition, Richard E. Mayer asks whether people learn more deeply when ideas are expressed in words and pictures rather than in words alone. He reviews twelve principles of instructional design that are based on experimental research studies and grounded in a theory of how people learn from words and pictures. The result is what Mayer calls the cognitive theory of multimedia learning, a theory introduced in the first edition of Multimedia Learning and further developed in The Cambridge Handbook of Multimedia Learning.
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Prior work has shown that self-explanation leads to greater learning gains and that those students who can clearly explain their solution process are more likely to generate good solutions. To our knowledge, no studies have measured the impact that self-explanation has on a student's solution process. In this work, we explore an unprecedented database containing digital records of the pen strokes from over 120 students' coursework over an entire quarter. This unique data allows for never before possible analyses of students' solution processes. In this paper, we compare the differences in class performance and solution processes between two groups of these students, one group that was required to supply handwritten self-explanations, another that was not. This comparison reveals that students who generate self-explanations along with their homework solutions perform better, learn core concepts better, and solve problems more like an expert than those students not required to generate self-explanation.
Article
Homework has long been a cornerstone of education, but is it actually worthwhile for a student to put effort into homework? In this paper we present novel techniques for examining correlations between students' effort on homework and their performance in a course. Students enrolled in a Mechanical Engineering Statics course at the University of California, Riverside were given Livescribe™ digital pens with which they completed their coursework, producing an electronic, time-stamped record of all of their work. We computed numerical features from these records to estimate the effort students expended on each homework assignment. We used these features to predict student performance on a number of measures, such as homework, quiz, and exam scores, and show that these effort-based features can explain up to 39.9% of the variance in student performance (i.e., R2 = 0.399). These effort-performance correlations offer insight into the types of transfer that occurs from homework to exam problems. Additionally, these results serve as a measure of the effectiveness of homework problems, providing instructors with a principled method for improving homework assignments for future course offerings.
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Students have long been taught that neatness counts. But does it? In this project, we seek to understand how the organization of a student's solution to a problem relates to the correctness of the work. Understanding this relationship will enable us to create software to provide early warnings to students who may be struggling in a course. In this study, students in an undergraduate statics course completed all of their work, including homework, quizzes, and exams, using Livescribe™ Smartpens. These devices record the solutions as time-stamped pen strokes, enabling us to see not only the final ink on the page, but also the order in which it was written. Using this unique database of student work, we examine how the history of the solution construction process correlates with the correctness of the work. We characterize solution histories with a number of quantitative features describing the temporal and spatial organization of the work. For example, there are features that describe the order in which various problem solving activities, such as the construction of free body diagrams and equilibrium equations, are performed, and the amount of time spent on each activity. The spatial organization of the work is characterized by the extent to which a student revisits earlier parts of a solution to revise their work. Regression models have demonstrated that, on average, about 40% of the variance in student performance could be explained by our features. This is a surprising result in that the features consider only the process of recording the solution history and do not actually consider the semantics of the writing.
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Homework exercises are a cornerstone of engineering education. In this work, we seek to understand how homework habits correlate with course performance. We conducted a study in which students in an undergraduate statics course used LivescribeTM smartpens to complete their coursework. Smartpens serve the same purpose as traditional ink pens, but also digitize the writing, producing a time-stamped record of the work. From this data, we compute a variety of quantitative features characterizing homework habits, such as the total amount of ink written and the time of the day at which the work is done. We use regression models to examine how these features correlate with the final course grade. We control for student ability using the students' scores on the Force Concept Inventory (FCI), which is completed during the first week of class. The FCI itself has some predictive ability, correlating with final course grade with R2 = 0.27. However, combining this score with the total ink written on the third homework assignment produced a much stronger correlation with R2 = 0.35. A regression model including the FCI score and four features computed from this assignment resulted in R2 = 0.43. Thus, by the end of the third week of the quarter, it is possible to explain a significant amount of the variance in final course grade by considering homework habits. This work provides a foundation for building early warning systems that examine homework activity to identify students at risk of performing poorly in a course.
Article
A "pencast" is a type of video presentation in which recorded digital ink and audio are replayed in synchronization. To create a pencast, a Livescribe™ "Smartpen" is used to record handwritten content with voice narration. An instructor can use a Smartpen to construct a pencast that replays the solution to a problem with synchronized explanation. Pencasts, are becoming a popular instructional tool, but their educational effectiveness has not been formally studied. Thus, we present a research study which compares the educational effectiveness of pencasts to that of traditional instructional media, specifically, electronic PDF documents. In each study session, students in a Statics course were given one of two tutorials. One tutorial was a pencast, the other was a PDF with identical content. Each session included a pre- and posttest to measure learning gains. The study comprised two sessions, one concerning belt friction problems, the other wedge friction problems. The study also included a survey of the students' opinions about the two types of instructional media. While the two treatments provided equivalent and significant learning gains, there was a clear preference among students for the pencast tutorials.
Article
We present an intelligent pen-based tutoring system for Statics - the sub-discipline of engineering mechanics concerned with the analysis of mechanical systems in equilibrium under the action of forces. The system scaffolds students in the construction of free body diagrams and equilibrium equations for planar devices comprised of one or more rigid bodies. While there has been extensive research in intelligent tutoring systems, most existing systems rely on traditional WIMP (Windows, Icons, Menus, Pointer) interfaces. With these systems, students typically select the correct problem solution from among a set of predefined solution elements. With our pen-based interface, by contrast, students are guided in constructing solutions from scratch, mirroring the way they solve problems in ordinary practice, which recent research suggests is particularly important for effective instruction. Our system embodies several innovations including a novel instructional technique that focuses students' attention on a system boundary as a tool for constructing free body diagrams, a tutorial feedback system based on "buggy-rules", and a hierarchical feedback system which promotes independent problem-solving skills. In winter 2010, the tutoring system was used by 100 students in an undergraduate Statics course at the University of California, Riverside. Results from pre- and posttests reveal measurable learning gains even after only a short exposure to the system. In an attitudinal survey, students reported that, while there is room for improvement, the interface was preferable to a WIMP interface and the methodology implemented in the system was valuable for learning Statics.
An emerging field of educational data mining (EDM) is building on and contributing to a wide variety of disciplines through analysis of data coming from various educational technologies. EDM researchers are addressing questions of cognition, metacognition, motivation, affect, language, social discourse, etc. using data from intelligent tutoring systems, massive open online courses, educational games and simulations, and discussion forums. The data include detailed action and timing logs of student interactions in user interfaces such as graded responses to questions or essays, steps in rich problem solving environments, games or simulations, discussion forum posts, or chat dialogs. They might also include external sensors such as eye tracking, facial expression, body movement, etc. We review how EDM has addressed the research questions that surround the psychology of learning with an emphasis on assessment, transfer of learning and model discovery, the role of affect, motivation and metacognition on learning, and analysis of language data and collaborative learning. For example, we discuss (1) how different statistical assessment methods were used in a data mining competition to improve prediction of student responses to intelligent tutor tasks, (2) how better cognitive models can be discovered from data and used to improve instruction, (3) how data-driven models of student affect can be used to focus discussion in a dialog-based tutoring system, and (4) how machine learning techniques applied to discussion data can be used to produce automated agents that support student learning as they collaborate in a chat room or a discussion board. WIREs Cogn Sci 2015, 6:333-353. doi: 10.1002/wcs.1350 For further resources related to this article, please visit the WIREs website. The authors have declared no conflicts of interest for this article. © 2015 John Wiley & Sons, Ltd.
Book
This unique and ground-breaking book is the result of 15 years research and synthesises over 800 meta-analyses on the influences on achievement in school-aged students. It builds a story about the power of teachers, feedback, and a model of learning and understanding. The research involves many millions of students and represents the largest ever evidence based research into what actually works in schools to improve learning. Areas covered include the influence of the student, home, school, curricula, teacher, and teaching strategies. A model of teaching and learning is developed based on the notion of visible teaching and visible learning. A major message is that what works best for students is similar to what works best for teachers - an attention to setting challenging learning intentions, being clear about what success means, and an attention to learning strategies for developing conceptual understanding about what teachers and students know and understand. Although the current evidence based fad has turned into a debate about test scores, this book is about using evidence to build and defend a model of teaching and learning. A major contribution is a fascinating benchmark/dashboard for comparing many innovations in teaching and schools.
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A study habits index measuring distractibility, inquisitiveness, and compulsiveness in test and homework situations was administered to 69 (27 women and 42 men) first-year, college engineering students. Scholastic Aptitude Test (SAT) scores and grade-point averages were also obtained. Two hypotheses were entertained: a) students who earn the highest grades, in contrast to less successful peers, will tend to be less distractible and more inquisitive; b) the advantage of such study habits will be evident when effects of academic aptitude (SAT performance) are controlled. Statistical tests supported both hypotheses. No significant sex differences were found for aptitude or grades, but women scored higher on the compulsiveness study habits scale than men. We argue that special programs to help some students study more meaningfully would likely improve overall academic performance.
Article
Background Across many domains, research has shown that students often fail to select and apply appropriate conceptual knowledge when solving problems. Programs designed to support monitoring skills have been successful in several domains. Purpose (Hypothesis) Critical conceptual knowledge in statics appears to be cued by paying attention to the bodies that are present in a problem, as well as to which ones are interacting and how. The research question addresses whether students can be induced to think about the bodies present, and whether focusing on bodies improves problem solving performance. Design/Method Using a pre-post test design, written and verbal protocols were obtained for students solving problems before and after instruction. During instruction all students saw the same set of examples and corrected answers, but only the experimental group was asked questions designed to promote body centered talk. Solutions and protocols were coded and analyzed for frequency of body centered talk and solution quality. Results The experimental group showed statistically significant increases in relevant body centered talk after instruction. Both groups improved their ability to represent unknown forces in free body diagrams after instruction, with the experimental group showing a greater, but not statistically significant, improvement. However, for both groups, the error rate in representing unknown forces at an interaction was significantly lower when a student referred to the bodies in the particular interaction. Conclusions Problem solving in conceptually rich domains can improve if, in addition to acquiring conceptual knowledge, students develop strategies for recognizing when and how to apply it.
Article
A quantification of conceptual understanding of students in statics was undertaken. Drawing on a prior study identifying the fundamental concepts and typical student errors in statics, multiple choice questions were devised to probe students' ability to use concepts in isolation. This paper describes a testing instrument comprising such questions, as well as psychometric analyses of test results of 245 students at five universities.
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Taking notes on laptops rather than in longhand is increasingly common. Many researchers have suggested that laptop note taking is less effective than longhand note taking for learning. Prior studies have primarily focused on students' capacity for multitasking and distraction when using laptops. The present research suggests that even when laptops are used solely to take notes, they may still be impairing learning because their use results in shallower processing. In three studies, we found that students who took notes on laptops performed worse on conceptual questions than students who took notes longhand. We show that whereas taking more notes can be beneficial, laptop note takers' tendency to transcribe lectures verbatim rather than processing information and reframing it in their own words is detrimental to learning.
Article
In this article, research conducted in the United States since 1987 on the effects of homework is summarized. Studies are grouped into four research designs. The authors found that all studies, regardless of type, had design flaws. However, both within and across design types, there was generally consistent evidence for a positive influence of homework on achievement. Studies that reported simple homework–achievement correlations revealed evidence that a stronger correlation existed (a) in Grades 7–12 than in K–6 and (b) when students rather than parents reported time on homework. No strong evidence was found for an association between the homework–achievement link and the outcome measure (grades as opposed to standardized tests) or the subject matter (reading as opposed to math). On the basis of these results and others, the authors suggest future research.
Article
Study habit, skill, and attitude inventories and constructs were found to rival standardized tests and previous grades as predictors of academic performance, yielding substantial incremental validity in predicting academic performance. This meta-analysis (N = 72,431, k = 344) examines the construct validity and predictive validity of 10 study skill constructs for college students. We found that study skill inventories and constructs are largely independent of both high school grades and scores on standardized admissions tests but moderately related to various personality constructs; these results are inconsistent with previous theories. Study motivation and study skills exhibit the strongest relationships with both grade point average and grades in individual classes. Academic specific anxiety was found to be an important negative predictor of performance. In addition, significant variation in the validity of specific inventories is shown. Scores on traditional study habit and attitude inventories are the most predictive of performance, whereas scores on inventories based on the popular depth-of-processing perspective are shown to be least predictive of the examined criteria. Overall, study habit and skill measures improve prediction of academic performance more than any other noncognitive individual difference variable examined to date and should be regarded as the third pillar of academic success. © 2008 Association for Psychological Science.
Article
This literature review concludes that, although time spent is not so consistently related to achievement as it may seem, studies of time use in schools provide important information that can serve as a base for school improvement. (MJL)
Article
The book is written with three goals in mind. First, I hope my conclusions help school administrators and teachers develop homework policies that benefit students. Second, I hope the review helps future homework researchers identify areas that are most in need of investigation. Finally, I hope the procedures I used to integrate the research prove instructive to others who are interested in making sense of social science literatures. I have tried to apply state-of-the-art techniques for gathering and integrating the homework research. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Article
Investigated the causal effects of homework time on high school seniors' achievement, as measured by grades, using the National Opinion Research Center (1980) High School and Beyond data set. As expected, study time contributed significantly to student grades, and within the model proposed, its direct effect was second only to that of intellectual ability. Further analysis indicated that with greater variability in study time and grades, the influence of homework might be even stronger. There is also evidence of current low homework demands by the schools and of the inflation of high school grades. It is suggested that increased homework demands and more stringent grading standards might increase both student achievement and confidence in the schools. (16 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Article
BACKGROUND Courseware for engineering education can feature many discrete interac-tive learning elements, and typically student usage is not compelled. To take advantage of such courseware, self-regulation of learning may be necessary. Evaluation of courseware should consider actual usage, learning gains, and indications of learning self-regulation. PURPOSE (HYPOTHESIS) The research question focuses on how students' interactions with the course-ware affect their learning gains. The hypothesis tested is that learning gains from online courseware increase with usage, and particularly with usage that suggests learning self-regulation. DESIGN/METHOD Students in a lecture-based statics course were assigned to study previously developed courseware as part of homework assignments. Learning gains were deduced from pre-and post-paper and pencil diagnostic quizzes, and from the first class exam. Credit was based on quiz scores, rather than courseware usage. Usage of interactive elements of the courseware was inferred from log files of students' interactions with the courseware, and pat-terns suggesting learning self-regulation were identified. RESULTS High, statistically significant learning gains were found. Substantial usage was evident, with core learning activities initiated by, on average, three-quarters of students. Learning gains and performance on the relevant class exam appeared to be more closely correlated with usage that indicated self-regulation of learning rather than with total usage of the courseware. CONCLUSIONS Methods of assessing courseware should go beyond courseware features, learning gains, and student self-reports of effectiveness to include monitor-ing of actual usage and analyses relating usage to learning. Self-regulation of learning is likely to be critical to successful usage of courseware, and courseware should be designed to encourage it.
Article
Earlier studies have suggested that higher education institutions could harness the predictive power of Learning Management System (LMS) data to develop reporting tools that identify at-risk students and allow for more timely pedagogical interventions. This paper confirms and extends this proposition by providing data from an international research project investigating which student online activities accurately predict academic achievement. Analysis of LMS tracking data from a Blackboard Vista-supported course identified 15 variables demonstrating a significant simple correlation with student final grade. Regression modelling generated a best-fit predictive model for this course which incorporates key variables such as total number of discussion messages posted, total number of mail messages sent, and total number of assessments completed and which explains more than 30% of the variation in student final grade. Logistic modelling demonstrated the predictive power of this model, which correctly identified 81% of students who achieved a failing grade. Moreover, network analysis of course discussion forums afforded insight into the development of the student learning community by identifying disconnected students, patterns of student-to-student communication, and instructor positioning within the network. This study affirms that pedagogically meaningful information can be extracted from LMS-generated student tracking data, and discusses how these findings are informing the development of a customizable dashboard-like reporting tool for educators that will extract and visualize real-time data on student engagement and likelihood of success.
Article
Discusses developments in the field of computer assisted instruction (CAI), focusing on the Stanford Project, initiated in 1964 under a grant from the Office of Education to develop and implement a CAI program in initial reading and mathematics. A progress report deals with the reading program with particular reference to the past school year, when for the first time a sizable group of students received a major portion of their daily reading instruction under computer control. The first year's operation must be considered essentially as an extended debugging of both the computer system and the curriculum materials. Nevertheless, some interesting comments can be made on the basis of this experience regarding both the feasibility of CAI and the impact of such instruction on the overall learning process. Parts of the project addressed include the CAI reading curriculum, problems in implementing the curriculum, and some results from the first year of operation with first grade students.
Article
The IMMEX (Interactive Multi-Media Exercises) Web-based problem set platform enables the online delivery of complex, multimedia simulations, the rapid collection of student performance data, and has already been used in several genetic simulations. The next step is the use of these data to understand and improve student learning in a formative manner. This article describes the development of probabilistic models of undergraduate student problem solving in molecular genetics that detailed the spectrum of strategies students used when problem solving, and how the strategic approaches evolved with experience. The actions of 776 university sophomore biology majors from three molecular biology lecture courses were recorded and analyzed. Each of six simulations were first grouped by artificial neural network clustering to provide individual performance measures, and then sequences of these performances were probabilistically modeled by hidden Markov modeling to provide measures of progress. The models showed that students with different initial problem-solving abilities choose different strategies. Initial and final strategies varied across different sections of the same course and were not strongly correlated with other achievement measures. In contrast to previous studies, we observed no significant gender differences. We suggest that instructor interventions based on early student performances with these simulations may assist students to recognize effective and efficient problem-solving strategies and enhance learning.
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This article explores handwriting recognition-based interfaces in intelligent tutoring systems for students learning algebra equations.
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