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Remitted psychotic depression (MDDPsy) has heterogeneity of outcome. The study's aims were to identify subgroups of persons with remitted MDDPsy with distinct trajectories of depression severity during continuation treatment and to detect predictors of membership to the worsening trajectory.
Method
One hundred and twenty-six persons aged 18–85 years participated in a 36-week randomized placebo-controlled trial (RCT) that examined the clinical effects of continuing olanzapine once an episode of MDDPsy had remitted with sertraline plus olanzapine. Latent class mixed modeling was used to identify subgroups of participants with distinct trajectories of depression severity during the RCT. Machine learning was used to predict membership to the trajectories based on participant pre-trajectory characteristics.
Results
Seventy-one (56.3%) participants belonged to a subgroup with a stable trajectory of depression scores and 55 (43.7%) belonged to a subgroup with a worsening trajectory. A random forest model with high prediction accuracy (AUC of 0.812) found that the strongest predictors of membership to the worsening subgroup were residual depression symptoms at onset of remission, followed by anxiety score at RCT baseline and age of onset of the first lifetime depressive episode. In a logistic regression model that examined depression score at onset of remission as the only predictor variable, the AUC (0.778) was close to that of the machine learning model.
Conclusions
Residual depression at onset of remission has high accuracy in predicting membership to worsening outcome of remitted MDDPsy. Research is needed to determine how best to optimize the outcome of psychotic MDDPsy with residual symptoms.
In Beyond CLIL: Pluriliteracies Teaching for Deeper Learning we demonstrate that learning progressions typically evolve around big ideas in a discipline or a subject. Disciplinary core constructs show how disciplines or subjects use different approaches to collecting, analysing, evaluating and communicating information. This is why, in PTDL, those core constructs are used to inform and guide the development of learning progressions into/for individual subjects. Progress in subject learning is not linear but multi-dimensional and multi-directional. It involves specific ways of thinking and typical forms of representing information and specific text types or genres to share information. Progress in subject learning can be conceptualised as enhancing meaning-making potential. It entails growing conceptual understanding of content knowledge as well as a growing command of subject-specific procedures and strategies. It results from engaging in the specific major activity domains of a subject (doing, organising, explaining and arguing). This idea is captured in Figure 3.1.
We have coined the term deeper learning episode (DLE) to emphasise the idea that PTDL’s overriding objective is to offer opportunities for deeper learning through a focus on subject-specific literacies. Also, we felt the need to introduce a new term that is not limited to specific timetabled lessons. A DLE extends over a series of lessons, depending not only on the intentions, purposes and outcomes of learning but, more importantly, on whether or not those intentions, purposes and outcomes translate into deeper learning. A DLE organically flows into the next one when learners can demonstrate deep understanding of the specific content or sufficient mastery of the targeted skills. Otherwise, it is time for us teachers to go back to the drawing board to design and offer more opportunities for learners to understand and practise. Each DLE consists of a number of interconnected phases where teachers and learners jointly engage in complimentary activities to incite and sustain deeper learning processes (see Figure 4.1).
For knowledge to become transferable, it needs to be stored in long-term memory in such a way that learners can successfully retrieve it. However, ‘merely’ committing information to long-term memory does not equal deeper learning. Teaching that focuses mostly on facts and does not provide learners with ample opportunities to use and apply their knowledge will lead to so-called inert knowledge, which cannot be accessed to solve problems. To really understand content, our learners need to establish connections between new information and prior knowledge, relate new information to larger contexts and understand its relevance inside and outside the classroom.
The pedagogic considerations and practical ideas presented in this volume offer, we believe, a compelling rationale for exploring the potential of deeper learning across a wide range of subjects through an explicit focus on subject literacies that:
Promote the prioritisation of deeper understanding of subject-specific concepts alongside subject-specific ways of constructing and communicating that understanding.
Illustrate how a continuous focus on meaning-making and languaging of understanding will not only support and facilitate conceptual understanding of subject content but will also render learning more transparent by making subject-specific thinking and processing visible and thus more accessible and easier to grasp for learners.
Emphasise that using more than one language in subject lessons to highlight subject-specific ways of meaning-making across languages can be a powerful learning catalyst. This is because a focus on PLURI-literacies will create synergies that will increase our learners’ meaning-making potential by empowering them to successfully communicate across subjects and languages, purposefully using a wide variety of genres, modes and styles.
Show that the concept of deeper learning episodes (DLEs) is compatible with a wide range of subjects. At the same time, the experts report that our suggested template for designing such episodes, along with the guiding questions we developed, constitute useful planning tools that offer practitioners effective, systematic and adaptable guidance in designing DLEs.
We hope that readers will be inspired to adapt their approach to learning by moving from focusing on ‘teaching students’ to mentoring their learners’ personal growth in explicit ways. PTDL suggests novel ways of rethinking and adapting teaching strategies by providing opportunities for building, applying and transferring understanding as it evolves. This will facilitate deeper learning and foster engagement, commitment and mastery orientation.
While understanding the mechanics of deeper learning is fundamental, deeper learning can only become a reality in our classrooms when we pay close attention to the drivers of deeper learning. This will allow learners to embrace a deeper learning mindset, which is required to develop academic tenacity or resilience, to work consistently over sustained periods of time, to engage in and master challenging tasks, to successfully interact with their peers and to self-regulate their learning.
If education is to prepare learners for lifelong learning, there needs to be a shift towards deeper learning: a focus on transferable knowledge and problem-solving skills alongside the development of a positive or growth mind-set. Deeper learning is inextricably linked with CLIL (Content and Language Integrated Learning) – a revolutionary teaching approach where students study subjects in a different language. Designed as a companion to the influential volume Beyond CLIL, this highly practical book offers step-by-step instruction for designing and implementing innovative tasks and materials for pluriliteracies development. It contains annotated case studies of deeper learning lesson plans across a wide range of school subjects, using an innovative and proven template, to help teachers explore the potential of deeper learning inside their own classrooms. Theoretically grounded, this book offers a roadmap for schools, ranging from exploratory first steps, to transdisciplinary projects, to whole school moves for curriculum development and transformative pedagogies.
A basic tenet of our pluriliteracies model is that deeper learning is fundamental for an individual’s learning progression and development. This is not new. However, closer investigation reveals the complex and dynamic nature of the processes involved. Whilst a great deal has been written about deeper learning and its importance for engaging learners ‘through discovering and mastering existing knowledge and then creating new knowledge’, (Fullan & Langworthy, 2014, p. 2), there is little to guide in-depth understanding of the nature of those processes – that is, what it means to master existing knowledge and create new knowledge which can then be ‘translated’ into pedagogic practices to support and ‘grow’ classroom learning. In seeking to understand better the nature of deeper learning and its implications for learning and teaching, two strands have emerged: the mechanics or cognitive-linguistic processes through which deeper learning evolves, and the drivers of and for deeper learning. We define drivers as those factors that promote or inhibit the processes or mechanics of deeper learning, such as student and teacher engagement.
Whilst the theoretical underpinning and rationale for deeper learning presented and discussed in Chapter 4 is based on current thinking and research by academics and educators, its interpretation, application and actual implementation is challenging. Understanding the mechanisms of deeper learning is one thing. Generating and sustaining learner commitment and achievement is another.