Strategy and narrative in higher education

In this paper we apply the idea of narrative to strategy and to the development of strategy in the higher education context. We explore how strategy is formed as an intertextual narrative in a comparative study of higher education (HE) in the UK context. Existing research suggests that competition between narratives, such as that in HE, should be problematic in strategy terms. We show that this is not necessarily the case. Unlike in other settings where new strategy narratives tend to drive out previous narratives, in HE it is the ongoing interaction between historical and new narratives that gives the content of strategy its essential voice. We show how apparently competing narratives are accommodated though appeals to emotion and values. The maintenance of strategic direction requires hope and a synthesis of societal values that maintains access to the past, the future, and multiple narrators. This approach helps us understand how universities perform the complex task of adapting the strengths of the university’s past to the challenges of external policy developments in strategy formation

A Unifying Framework for Combining Complementary Strengths of Humans and ML toward Better Predictive Decision-Making

Hybrid human-ML systems are increasingly in charge of consequential decisions in a wide range of domains. A growing body of empirical and theoretical work has advanced our understanding of these systems. However, existing empirical results are mixed, and theoretical proposals are often mutually incompatible. In this work, we propose a unifying framework for understanding conditions under which combining the complementary strengths of humans and ML leads to higher quality decisions than those produced by each of them individually -- a state which we refer to as human-ML complementarity. We focus specifically on the context of human-ML predictive decision-making and investigate optimal ways of combining human and ML predictive decisions, accounting for the underlying sources of variation in their judgments. Within this scope, we present two crucial contributions. First, taking a computational perspective of decision-making and drawing upon prior literature in psychology, machine learning, and human-computer interaction, we introduce a taxonomy characterizing a wide range of criteria across which human and machine decision-making differ. Second, formalizing our taxonomy allows us to study how human and ML predictive decisions should be aggregated optimally. We show that our proposed framework encompasses several existing models of human-ML complementarity as special cases. Last but not least, an initial exploratory analysis of our framework presents a critical insight for future work in human-ML complementarity: the mechanism by which we combine human and ML judgments should be informed by the underlying causes of divergence in their decisions.Comment: 21 pages, 1 figur

Towards a Learner-Centered Explainable AI: Lessons from the learning sciences

In this short paper, we argue for a refocusing of XAI around human learning goals. Drawing upon approaches and theories from the learning sciences, we propose a framework for the learner-centered design and evaluation of XAI systems. We illustrate our framework through an ongoing case study in the context of AI-augmented social work.Comment: 7 pages, 2 figure

Ethics of AI in Education: Towards a Community-Wide Framework

While Artificial Intelligence in Education (AIED) research has at its core the desire to support student learning, experience from other AI domains suggest that such ethical intentions are not by themselves sufficient. There is also the need to consider explicitly issues such as fairness, accountability, transparency, bias, autonomy, agency, and inclusion. At a more general level, there is also a need to differentiate between doing ethical things and doing things ethically, to understand and to make pedagogical choices that are ethical, and to account for the ever-present possibility of unintended consequences. However, addressing these and related questions is far from trivial. As a first step towards addressing this critical gap, we invited 60 of the AIED community’s leading researchers to respond to a survey of questions about ethics and the application of AI in educational contexts. In this paper, we first introduce issues around the ethics of AI in education. Next, we summarise the contributions of the 17 respondents, and discuss the complex issues that they raised. Specific outcomes include the recognition that most AIED researchers are not trained to tackle the emerging ethical questions. A well-designed framework for engaging with ethics of AIED that combined a multidisciplinary approach and a set of robust guidelines seems vital in this context

Erythropoietin Couples Hematopoiesis with Bone Formation

It is well established that bleeding activates the hematopoietic system to regenerate the loss of mature blood elements. We have shown that hematopoietic stem cells (HSCs) isolated from animals challenged with an acute bleed regulate osteoblast differentiation from marrow stromal cells. This suggests that HSCs participate in bone formation where the molecular basis for this activity is the production of BMP2 and BMP6 by HSCs. Yet, what stimulates HSCs to produce BMPs is unclear.In this study, we demonstrate that erythropoietin (Epo) activates Jak-Stat signaling pathways in HSCs which leads to the production of BMPs. Critically, Epo also directly activates mesenchymal cells to form osteoblasts in vitro, which in vivo leads to bone formation. Importantly, Epo first activates osteoclastogenesis which is later followed by osteoblastogenesis that is induced by either Epo directly or the expression of BMPs by HSCs to form bone.These data for the first time demonstrate that Epo regulates the formation of bone by both direct and indirect pathways, and further demonstrates the exquisite coupling between hematopoiesis and osteopoiesis in the marrow

What Might Have Been Lost

This article examines the role of “independent” folk music (indie-folk) in personal identity formation. It builds upon Paul Ricoeur’s theory of narrative identity, which argues (i) that it is through the mechanism of narrative that people build a more or less coherent life-story, and (ii) emphasizes the role of art (most notably literary fiction and poetry) as a mediator in the comprehension and regulation of transitory life experiences. This article aims to apply these insights to studying the role of indie-folk, a narrative art form adhering to the traditional understanding of folk music as a genre rooted in oral tradition, in the construction of personal identity. Studying the daily use of indie-folk songs by audience members through in-depth interviewing, it shows that (i) the reception of indie-folk music results in ritualistic listening behavior aimed at coping with the experience of accelerating social time; (ii) that respondents use indie-folk narratives as resources for reading the self, and (iii) that indie-folk songs provide healing images that are effective in coping with the experience of narrated time as discordant. In arguing for the central role of narrative in identity formation, this article aims to contribute to existing research on music as a “technology of the self” (DeNora). It specifically emphasizes how narrative particles are tools and building blocks in identity construction, a process characterized by the oscillation between narrative coherence and disruption

Generational Association Studies of Dopaminergic Genes in Reward Deficiency Syndrome (RDS) Subjects: Selecting Appropriate Phenotypes for Reward Dependence Behaviors

Abnormal behaviors involving dopaminergic gene polymorphisms often reflect an insufficiency of usual feelings of satisfaction, or Reward Deficiency Syndrome (RDS). RDS results from a dysfunction in the “brain reward cascade,” a complex interaction among neurotransmitters (primarily dopaminergic and opioidergic). Individuals with a family history of alcoholism or other addictions may be born with a deficiency in the ability to produce or use these neurotransmitters. Exposure to prolonged periods of stress and alcohol or other substances also can lead to a corruption of the brain reward cascade function. We evaluated the potential association of four variants of dopaminergic candidate genes in RDS (dopamine D1 receptor gene [DRD1]; dopamine D2 receptor gene [DRD2]; dopamine transporter gene [DAT1]; dopamine beta-hydroxylase gene [DBH]). Methodology: We genotyped an experimental group of 55 subjects derived from up to five generations of two independent multiple-affected families compared to rigorously screened control subjects (e.g., N = 30 super controls for DRD2 gene polymorphisms). Data related to RDS behaviors were collected on these subjects plus 13 deceased family members. Results: Among the genotyped family members, the DRD2 Taq1 and the DAT1 10/10 alleles were significantly (at least p < 0.015) more often found in the RDS families vs. controls. The TaqA1 allele occurred in 100% of Family A individuals (N = 32) and 47.8% of Family B subjects (11 of 23). No significant differences were found between the experimental and control positive rates for the other variants. Conclusions: Although our sample size was limited, and linkage analysis is necessary, the results support the putative role of dopaminergic polymorphisms in RDS behaviors. This study shows the importance of a nonspecific RDS phenotype and informs an understanding of how evaluating single subset behaviors of RDS may lead to spurious results. Utilization of a nonspecific “reward” phenotype may be a paradigm shift in future association and linkage studies involving dopaminergic polymorphisms and other neurotransmitter gene candidates