Machine learning systems are infiltrating our lives and are beginning to become important in our education systems. This article, developed from a synthesis and analysis of previous research, examines the implications of recent developments in machine learning for human learners and learning. In this article we first compare deep learning in computers and humans to examine their similarities and differences. Deep learning is identified as a sub-set of machine learning, which is itself a component of artificial intelligence. Deep learning often depends on backwards propagation in weighted neural networks, so is non-deterministic—the system adapts and changes through practical experience or training. This adaptive behaviour predicates the need for explainability and accountability in such systems. Accountability is the reverse of explainability. Explainability flows through the system from inputs to output (decision) whereas accountability flows backwards, from a decision to the person taking responsibility for it. Both explainability and accountability should be incorporated in machine learning system design from the outset to meet social, ethical and legislative requirements. For students to be able to understand the nature of the systems that may be supporting their own learning as well as to act as responsible citizens in contemplating the ethical issues that machine learning raises, they need to understand key aspects of machine learning systems and have opportunities to adapt and create such systems. Therefore, some changes are needed to school curricula. The article concludes with recommendations about machine learning for teachers, students, policymakers, developers and researchers.

Recommender systems for technology-enhanced learning are examined in relation to learners’ agency, that is, their ability to define and pursue learning goals. These systems make it easier for learners to access resources, including peers with whom to learn and experts from whom to learn. In this systematic review of the literature, we apply an Evidence for Policy and Practice Information (EPPI) approach to examine the context in which recommenders are used, the manners in which they are evaluated and the results of those evaluations. We use three databases (two in education and one in applied computer science) and retained articles published therein between 2008 and 2018. Fifty-six articles meeting the requirements for inclusion are analyzed to identify their approach (content-based, collaborative filtering, hybrid, other) and the experiment settings (accuracy, user satisfaction or learning performance), as well as to examine the results and the manner in which they were presented. The results of the majority of the experiments were positive. Finally, given the results introduced in this systematic review, we identify future research questions.

<p style="text-align: justify;">Teacher agency is a set of actions that a teacher takes beyond what is generally expected of them. The concept merits examination, as agency can bolster teachers’ ability to set and achieve professional development goals. To better understand how to study, and use, this relatively new concept in the academic literature, a systematic review of 164 publications written by researchers from 41 countries was conducted in order to document the research approaches used to study teacher agency, the participants whose agency was documented in a school setting, the methodology used and the type of analysis performed. The study found that teacher agency has been documented qualitatively in the form of case studies comprising interviews of a small number of individuals, with no consensus in terms of interview protocol. In most cases, the results are analyzed using emergent coding. The way that agency is documented varies but is most often underpinned by an ecological approach.</p>

Les enseignantes et les enseignants du collégial partagent avec les établissements d’enseignement la responsabilité de leur développement professionnel. Cet article a trait au codesign d’une plateforme numérique au service de l’agentivité des enseignants en situation de développement professionnel. Le processus de codesign a inclus le repérage des freins au développement professionnel ainsi que l’identification des buts de niveau motivationnel. Les résultats mettent l’accent sur les buts fonctionnels et opérationnels qui permettent de soutenir les buts de niveau motivationnel.College teachers share with educational institutions the responsibility for their professional development. This article deals with the co-design of a digital platform supporting teachers’ agency in the context of their professional development. The co-design process included the identification of barriers to professional development, as well as the identification of goals at the motivational level. Results emphasize the functional and operational goals supporting motivational goals.

Depuis le début de la pandémie, le personnel enseignant a dû s’adapter à différentes modalités d’enseignement et d’apprentissage. Dans un contexte en mouvance, il apparait important de s’intéresser à l’engagement du personnel enseignant, puisqu’il pourrait influencer positivement celui de leurs élèves (Klassen et al., 2013; Roth et al., 2007). L’engagement des personnes sur le marché du travail est étudié selon trois dimensions : l’absorption, la vigueur et le dévouement (Schaufeli et al., 2006). En contexte scolaire, une attention est portée à la dimension socioaffective de l’engagement, soit l’énergie consacrée à établir des relations avec les élèves et avec les collègues (Klassen et al., 2013). Le repérage de ces dimensions est important pour comprendre la variation de l’engagement du personnel enseignant, d’autant plus dans un contexte d’adaptation de l’enseignement et de l’apprentissage en raison du virage inopiné en formation à distance (FAD). Dans des entretiens semi-dirigés auprès de membres du personnel enseignant, nous nous sommes intéressés à leur engagement lorsque l’enseignement passe en FAD. Nos résultats permettent de cibler des situations ou des personnes qui influencent leur engagement : les ajustements successifs, les conditions technopédagogiques variables, le soutien inégal des parents ainsi qu’un sentiment d’isolement social et pédagogique.