On Bullshit

A number of leading academicians and practicing professionals in the fields of instructional and human performance technology (HPT) have observed over recent years a disturbing pattern of ideas and propositions being advocated by academics and practitioners alike that do not always ring true. At best some of these propositions are misleading, and at worst they are disingenuous. In some cases these propositions are deemed nonsense. In trying to understand this perpetuation of nonsense in instructional and performance technologies, we have found and use for reference a small book by Harry G. Frankfurt titled On Bullshit. This 2005 publication traces the meanings of BS and argues that it is a form of falsehood that is different than lying. Frankfurt disdains nonsense talk and relates it to the now popular concept of a bull session. We hope that you, like us, find Frankfurt's treatise on BS useful in understanding and differentiating between sense and nonsense in the fields of instructional and performance technologies. On Bullshit (2005; 67 pages; ISBN 10:0691122946; hardback $9.95) is published by Princeton University Press.     

智能机器人及其教学应用:创新意蕴与现实挑战

第三次人工智能浪潮的到来,掀起了人类历史上最具颠覆性的智能化革命,深刻地改变了技术与人的关系。以人工智能(AI)为驱动力的智能机器人技术,正逐步进入学校与课堂,赋能教学创新,催生了整合智能机器人的新型教学与学习生态系统。基于此,从智能机器人的内涵出发,梳理智能机器人的发展特征与教学功能;结合智能机器人的教学应用典型案例,审视智能机器人作为教学赋能之要素。通过计算与连接工具,探讨智能机器人助力精准教学、实践创新能力培养,开展智慧学伴、智能测评、大数据教学过程管理、人机协同"双师课堂"等方面的教学应用;从教学系统变革层面出发,以智能机器人为增强要素,促进教育系统的协同与治理;支持教师数字素养提升,补偿性教育技术应用,教学管理智能化,助力学校教育"智"理等;同时,结合现实提出智能机器人在教学应用中面临的五大挑战,以期为智能时代智能机器人的教育应用与发展,提供参考与思路。     

Contextualizing learning in the World Wide Web: accounting for the impact of culture

There is a developing urgency about how Australian universities should best make use of the World Wide Web (WWW) to meet the needs of culturally diverse students, especially those of Asian origin. This urgency is the result of both global and internal pressures, some political, (consider the imminence of the 'Hanson movement' against a multicultural Australia), some economic, some social. Moreover, to miss the current opportunity to exploit WWW technologies to design, implement and market effective instructional courses will only work to disadvantage Australia in a growing but globally competitive educational market. One of the most pressing problems in this context is how to provide instructional materials in a model of teaching and learning that is cost-effective, makes extensive use of WWW technologies to provide for flexibility in learning, and that is culturally appropriate. This paper outlines an approach and model for investigating and developing culturally appropriate instructional materials. It is hoped that through publication, we might obtain feedback on the validity of this model and also widen the potential of its scope beyond its immediate application to the Australian context. The following hypotheses are central to our work: (1) Existing cultural influences in instructional materials designed and delivered on the WWW by Australian universities, and intended for use by culturally diverse students, are minimal and ineffective. (2) The efficacy of learning based in the use of the WWW for instructional purposes can be improved by the adoption of a culturally appropriate model of instructional design. (3) Culture is a significant factor in determining the effectiveness of learning materials created in the WWW and intended for use by culturally diverse students. In testing these hypotheses, we intend to provide the empirical research to help determine the most appropriate ways of using the WWW to stimulate effective learning at tertiary level for all learners, whatever their cultural heritage or perspectives     

Evaluating the evidence base for evidence-based instructional practices in chemistry through meta-analysis

Investigating the effectiveness of instructional practices provides an evidence base to inform instructional decisions. Synthesizing research studies on instructional effectiveness provides an estimate of the generalizability of effectiveness across settings, along with an exploration of factors that may moderate the impact, which cannot be achieved within individual studies. This study sought to provide a synthesis of evidence-based instructional practices (EBIPs) particular to chemistry through meta-analysis. Ninety-nine studies were analyzed comprising a broader view of chemistry specific studies than past meta-analyses. The results showed that EBIPs feature a demonstrably positive impact on students' academic performance in chemistry, although assessment topic coverage and setting size emerged as relevant moderators of impact and prevented making definitive conclusions of the relative impact of each EBIP. In examining publication bias, an asymmetric distribution of studies based on standard error (SE) and effect size was found, indicative of potential publication bias. To explore the potential impact of bias, the trim and fill method was employed resulting in a range for the overall weighted effect size from 0.29 to 0.62. The study concludes that evidence-based instructional practices have demonstrated effectiveness even in consideration of potential publication bias, as the range of effect sizes remains positive, but highlights the continued need to publish null findings in the research literature.     

An analysis of the scholarly productivity of instructional technology faculty

The resumés of 132 instructional technology (IT) faculty were analyzed in an effort to assess the scholarly productivity of IT faculty. Faculty were surveyed to identify leading basic and applied IT journals and to determine overall publication patterns. The findings indicate that individuals who are now full professors have a higher publication rate than faculty at lower ranks in instructional systems/educational technology programs. On an annual basis, individuals in educational psychology programs published more applied, basic, and overall research than faculty in other academic affiliations.     

A comprehensive model covering prospective teachers’ technology use: the relationships among self,teaching and learning conceptions and attitudes

ABSTRACT

This study was conducted to propose and examine a comprehensive model embracing prospective teachers’ beliefs about self, teaching and learning conceptions, and attitudes towards using instructional technologies. Prospective or inservice teachers’ beliefs (from central to peripheral) and attitudes may empower the support of teacher training related to using instructional technologies. Participants were 1208 Turkish prospective teachers from five different universities who participated voluntarily and were selected using convenience sampling. Structural equation modelling analysis showed that prospective teachers’ beliefs about the autonomous self and autonomous–related self positively related to both their constructivist beliefs about teaching and learning and attitudes towards using instructional technologies, whereas the same beliefs negatively related to traditional conceptions of teaching and learning. Moreover, participants’ constructivist beliefs about teaching and learning positively predicted their attitudes towards using instructional technologies. Furthermore, their beliefs about related self positively and unexpectedly predicted their constructivist conceptions and attitudes. Implications are presented considering these results.     

In support of a functional definition of interaction

Abstract

A common theme in the distance education literature is that delivery technologies with the potential for real‐time interactivity will improve both the image and practice of distance education. Although there appears to be a growing acceptance of a causal relationship between system interactivity and instructional interaction, neither concept has been clearly or functionally defined. This article discusses several systems models and relates them to the contexts of instructional delivery, instructional design, instructional theory, and learning theory in an attempt to establish conceptual parameters for the function of interaction.     

E‐Learning Ain't Performance: Reviving HPT in an Era of Agile and Lean

Not all instructional design models are fully integrated into the HPT practice. Some of these processes such as the successive approximation model (SAM) and the lot like Agile methods approach (LLAMA) are the outgrowth of Agile processes for instructional design. The major design processes are often assumed to be competitive; that is, one model is better than the other. However, most Agile instructional design processes assume that the most ubiquitous performance solution is e‐learning, hence the direct integration of Agile processes. Therefore, instead of thinking about design models, we think about instructional methods and solutions over human performance technology (HPT). These methods are all equal until one understands the conditions or the context of the instructional or performance problem. We recommend designers reverse engineer the Agile instructional systems design process by using a rapid performance analysis method that quickly pinpoints and confirms the performance problem(s).     

Imaginaries and problematisations: A heuristic lens in the age of artificial intelligence in education

This paper suggests that artificial intelligence in education (AIEd) can be fruitfully analysed as ‘policies frozen in silicon’. This means that they exist as both materialised and proposed problematisations (problem representations with corresponding solutions). As a theoretical and analytical response, this paper puts forward a heuristic lens that can provide insights into how AI technologies (or advocated AI technologies) function as proposed solutions to certain problematisations based on various imaginaries about how education and learning are best performed or supported. The combined reading of imaginaries and problematisations can thereby aid in our understanding of why and how visions of learning and education are framed in relation to AIEd developments. The overall ambition is to advance theoretical and analytical approaches towards an educational system which is (anticipated as) increasingly permeated by AI systems—systems that also support and implement, more or less, invisible models, standards and assessments of learning, as well as more grand visions of (technology-augmented) education in society.

Practitioner notes

What is already known about this topic

• Artificial intelligence in education (AIEd) is repeatedly presented as a solution for a range of educational ‘problems’.
• This means that such ‘solutions’ must also frame certain aspects as ‘problems’.
• Such problems and ‘solutions’ (problematisations) also exist within certain imaginaries of the present times and of the future, where these problematisations are presented as particularly significant and acute, and promoting specific anticipations of learning and ideals of education.

What this paper adds

• An exposition of problematisations in educational settings.
• An exposition of educational imaginaries.
• A heuristic lens for understanding the ‘present’ and ‘future’ in a particular imaginary as entangled in, and dependent on, a certain ‘past’.

Implications for practice and/or policy

• The approach presented in this paper provides a heuristic lens for examining how AI technologies (or advocated AI technologies) function as proposed solutions to problematisations based on imaginaries about how education and learning are best performed or supported.
• This aids our understanding of how and why certain visions of learning and education are framed in relation to AIEd developments (real or imagined).
• It also advances theoretical and analytical approaches towards an educational system, which is (anticipated as) increasingly permeated by AI systems—systems that also support and implement, more or less, invisible models, standards and assessments of learning, as well as more grand visions of (technology-augmented) education in society.

     

The effectiveness of man-machine tutorial dialogues for teaching attribute blocks problem-solving skills with an Artificial Intelligence CAI System

Two prominent design features of an Artificial Intelligence CAI (AI CAI) System for teaching problem-solving skills were experimentally evaluated. Seventy-six subjects were randomly assigned to four treatment groups formed by factorially combining two values each of two system design variables. The variables were the ability/lack of ability of the system to answer specific student generated questions regarding the current problem state, and the inclusion/exclusion of computer generated tutorial dialogues aimed at correcting student misprocessing of information during the solution of the problem. Analysis of the data indicated that students receiving computer generated instructional dialogues performed significantly more poorly on a transfer task than students not receiving such tutorial dialogues. The variable of student initiated questioning and the interaction of the two variables yielded non-significant results. Goal-directed production systems are reported to formally model task performance and instructional decision-making activities. The usefulness of these formal modeling techniques to instructional system design and evaluation is illustrated in a detailed analysis of the system's performance. Implications for future instructional system design techniques using the AI CAI approach are discussed.This article is based upon the author's unpublished doctoral dissertation.     

The mediating effects of needs satisfaction on the relationships between prior knowledge and self-regulated learning through artificial intelligence chatbot

The anthropomorphic characteristics of artificial intelligence (AI) can provide a positive environment for self-regulated learning (SRL). The factors affecting adolescents' SRL through AI technologies remain unclear. Limited AI and disciplinary knowledge may affect the students' motivations, as explained by self-determination theory (SDT). In this study, we examine the mediating effects of needs satisfaction in SDT on the relationship between students' previous technical (AI) and disciplinary (English) knowledge and SRL, using an AI conversational chatbot. Data were collected from 323 9th Grade students through a questionnaire and a test. The students completed an AI basic unit and then learned English with a conversational chatbot for 5 days. Confidence intervals were calculated to investigate the mediating effects. We found that students' previous knowledge of English but not their AI knowledge directly affected their SRL with the chatbot, and that satisfying the need for autonomy and competence mediated the relationships between both knowledge (AI and English) and SRL, but relatedness did not. The self-directed nature of SRL requires heavy cognitive learning and satisfying the need for autonomy and competence may more effectively engage young children in this type of learning. The findings also revealed that current chatbot technologies may not benefit students with relatively lower levels of English proficiency. We suggest that teachers can use conversational chatbots for knowledge consolidation purposes, but not in SRL explorations.

Practitioner notes

What is already known about this topic

• Artificial intelligence (AI) technologies can potentially support students' self-regulated learning (SRL) of disciplinary knowledge through chatbots.
• Needs satisfaction in Self-determination theory (SDT) can explain the directive process required for SRL.
• Technical and disciplinary knowledge would affect SRL with technologies.

What this paper adds

• This study examines the mediating effects of needs satisfaction in SDT on the relationship between students' previous AI (technical) and English (disciplinary) knowledge and SRL, using an AI conversational chatbot.
• Students' previous knowledge of English but not their AI knowledge directly affected their SRL with the chatbot.
• Autonomy and competence were mediators, but relatedness was not.

Implications for practice and/or policy

• Teachers should use chatbots for knowledge consolidation rather than exploration.
• Teachers should support students' competence and autonomy, as these were found to be the factors that directly predicted SRL.
• School leaders and teacher educators should include the mediating effects of needs satisfaction in professional development programmes for digital education.

     

Learning theories for artificial intelligence promoting learning processes

This paper discusses a three-level model that synthesizes and unifies existing learning theories to model the roles of artificial intelligence (AI) in promoting learning processes. The model, drawn from developmental psychology, computational biology, instructional design, cognitive science, complexity and sociocultural theory, includes a causal learning mechanism that explains how learning occurs and works across micro, meso and macro levels. The model also explains how information gained through learning is aggregated, or brought together, as well as dissipated, or released and used within and across the levels. Fourteen roles for AI in education are proposed, aligned with the model's features: four roles at the individual or micro level, four roles at the meso level of teams and knowledge communities and six roles at the macro level of cultural historical activity. Implications for research and practice, evaluation criteria and a discussion of limitations are included. Armed with the proposed model, AI developers can focus their work with learning designers, researchers and practitioners to leverage the proposed roles to improve individual learning, team performance and building knowledge communities.

Practitioner notes

What is already known about this topic

• Numerous learning theories exist with significant cross-over of concepts, duplication and redundancy in terms and structure that offer partial explanations of learning.
• Frameworks concerning learning have been offered from several disciplines such as psychology, biology and computer science but have rarely been integrated or unified.
• Rethinking learning theory for the age of artificial intelligence (AI) is needed to incorporate computational resources and capabilities into both theory and educational practices.

What this paper adds

• A three-level theory (ie, micro, meso and macro) of learning that synthesizes and unifies existing theories is proposed to enhance computational modelling and further develop the roles of AI in education.
• A causal model of learning is defined, drawing from developmental psychology, computational biology, instructional design, cognitive science and sociocultural theory, which explains how learning occurs and works across the levels.
• The model explains how information gained through learning is aggregated, or brought together, as well as dissipated, or released and used within and across the levels.
• Fourteen roles for AI in education are aligned with the model's features: four roles at the individual or micro level, four roles at the meso level of teams and knowledge communities and six roles at the macro level of cultural historical activity.

Implications for practice and policy

• Researchers may benefit from referring to the new theory to situate their work as part of a larger context of the evolution and complexity of individual and organizational learning and learning systems.
• Mechanisms newly discovered and explained by future researchers may be better understood as contributions to a common framework unifying the scientific understanding of learning theory.

     

AI in education: learner choice and fundamental rights

ABSTRACT

This article examines benefits and risks of Artificial Intelligence (AI) in education in relation to fundamental human rights. The article is based on an EU scoping study [Berendt, B., A. Littlejohn, P. Kern, P. Mitros, X. Shacklock, and M. Blakemore. 2017. Big Data for Monitoring Educational Systems. Luxembourg: Publications Office of the European Union. https://publications.europa.eu/en/publication-detail/-/publication/94cb5fc8-473e-11e7-aea8-01aa75ed71a1/]. The study takes into account the potential for AI and ‘Big Data’ to provide more effective monitoring of the education system in real-time, but also considers the implications for fundamental human rights and freedoms of both teachers and learners. The analysis highlights a need to balance the benefits and risks as AI tools are developed, marketed and deployed. We conclude with a call to embed consideration of the benefits and risks of AI in education as technology tools into the development, marketing and deployment of these tools. There are questions around who – which body or organisation – should take responsibility for regulating AI in education, particularly since AI impacts not only data protection and privacy, but on fundamental rights in general. Given AI’s global impact, it should be regulated at a trans-national level, with a global organisation such as the UN taking on this role.     

Ten Key Principles Supporting the Accomplishment-Based Curriculum Development Model

The Accomplishment-Based Curriculum Development system is based on ten key principles which provide a cohesive framework for the processes of human performance technology and instructional design. These ten principles are: the performance equation; the basic progression of performance; the processes of front-end analysis; the three types of behavior characteristics; logic regarding information storage and retrieval; the general tactics of instruction; the relationship of learning problems and special instructional tactics; the process of instructional design; the progression of materials development; and the relationship between analysis and evaluation. This article provides an overview of each principle, outlines the connections that link the individual principles into an integrated system of human performance technology and instructional design, and compares these principles to the work of others in the field. Finally, this article serves to document the contributions made by Joe Harless to the disciplines of human performance technology and to instructional design.     

Emotional AI and EdTech: serving the public good?

ABSTRACT

Education Technology (EdTech) companies are deploying emotional AI to quantify social and emotional learning. Focusing on facial coding emotional AI that uses computer vision and algorithms to see, recognise, categorise and learn about facial expressions of emotion, this paper evaluates nascent usage of these technologies in education. To do this, it assesses the nature of child rights, the history and modern usage of face-based emotional AI, methodology and efficacy, and what this paper sees as a clash of private and public interests. Concern is shown to be two-fold: first is on method, especially given scope for material effects on students; second are ethical and legal concerns. While proposing a list of considerations for any implementation for these technologies in the classroom, the paper concludes that significant risks exist in deployment of these technologies in the classroom.     

An analysis of instructional design and technology departments

In this paper we explore the academic productivity of universities in the field of Instructional Design and Technology according to three types of publically available data: (1) publication in 20 leading journals, (2) representation in the Handbook of Educational Communications Technology, and (3) reception of professional organization awards. We then identified five of these programs and analyzed the attributes of these departments to better understand the trends in how they are preparing the instructional technologists of tomorrow. While previous studies have compared leading instructional design and technology (IDT) programs by looking at publication in a single journal, we believe our approach provides a well-rounded perspective. In addition, we hope this study helps IDT departments learn from each other by discussing trends in department curricula.     

Using Digital Technologies to Automate Instructional Design

Abstract

Recent advances in computer‐based interactive digital technologies have presented a broad range of possibilities to create powerful presentations and instructional messages. However, incorporating these new technologies effectively is not a simple task because the design of effective computer‐based instructional material is a complex process. Successful design involves interactions between individual learner characteristics, instructional delivery media, the type of specific knowledge and skills being taught, and the strategies and methods used to teach the material.

This paper discusses:

• — The need for an Automated Instructional Design

• — The integration of learning theory, instructional design and technology

• — Some approaches for automating instructional design

• — An exploration of ID Expert, ? an intelligent computer‐based multimedia instructional development system (beta version 1.0).

     

Special Needs, Special Issues

Services for children and young people with special educational needs are not well prepared for responding to the National Curriculum, local management of schools and other challenges of the 1990s. This is the conclusion drawn by HM Inspectorate (HMI) in the latest booklet in their Education Observed series entitled Special Needs Issues. The survey, which was published by the HMSO in June at £2.95, looks at developments in special educational provision which have occurred in England since the publication of the Warnock Report in 1978.