Deeply Digital Texts

Contributors: Chad Dorsey

The promise of digital textbooks is commonly cited from school districts and states alike. However, initiatives and examples cited as digital textbooks frequently amount to little more than direct porting of a static text to a digital medium, occasionally including basic embellishments such as video or interactive images. Such examples do indeed represent a “digitization” of learning materials, but they would fall distinctly in the shallow end of any continuum measuring how thoroughly they embrace the potential of digital media to transform learning.

In contrast, the concept of deeply digital texts includes the set of examples, use cases, and visions that together comprise the opposing pole of this continuum. Taken as a whole, the variety of materials, elements, and ideas that make the most complete use possible of the new possibilities enabled by interconnectivity and the affordances of digital media form a collective sketch of what the future of digital learning can offer. In this way, the term “deeply digital texts” is a misnomer, since the products of this transformed learning will bear little resemblance to today’s textbooks. Instead, the concepts of deeply digital curricula or even deeply digital learning may be more useful as a big idea.

The following collection of ideas is by nature amorphous, and no single example of deeply digital texts available today brings them together into a comprehensive offering. Indeed, this collection of ideas tends toward deeply digital learning. Here are some examples and visions that we should expect to see represented as deeply digital curricula for learning begin to cohere. Many of these ideas will be especially important in supporting STEM learning:

  • Embedded models and simulations to enable “digital inquiry”
    Probeware to make real-time data available when relevant to the curriculum
  • Seamless data-sharing to facilitate fluid scientific discourse
  • Student progress data for teachers to permit quick and efficient teaching adjustments
  • Monitoring and feedback support for students and to individualize learning
  • Flexible and adaptive presentation of curricula to enhance teacher support
  • Curricula can be customized and refined based on extensive data gathered from use by students
  • Curricula to provide in-depth experience with vital, cross-cutting concepts

Transformative Potential

With many or all of the idealized features (see above) in place in a deeply digital curriculum, learning would be truly transformed from the standpoint of both teachers and students. Learning experiences that are currently isolating for students and overwhelming for teachers could be personalized to suit the needs of individual students much more closely, and provide teachers rich, targeted information about student learning as it progresses. Of course, this would require the development and inclusion of an assessment system in the deeply digital learning system. As they receive individualized instruction in a variety of forms, students can learn more quickly and remain much more engaged in learning. With student needs identified by the assessment system, teachers can more efficiently work to address student needs and gain more satisfaction overall. Additionally, deeply digital curricula will provide a greatly smoothed path to existing inquiry learning areas while also unlocking possibilities for inquiry across a wide new variety of content areas.


With a full implementation of deeply digital curricula, students can design and conduct investigations into practically any concept. For example, they could learn through experiment, in the real world, by connecting probes and sensors to the system, or they could dive into concepts only accessible via virtual means as they manipulate molecules, direct the division of DNA, or capture the complexities of climate change. In this mode of inquiry, students would take on roles much closer to those of actual scientists, exploring phenomena, taking data, specializing in certain areas, and building knowledge through scientific argumentation based on evidence. Their deeply digital curricula will guide and scaffold the process as needed for the level of each student. As students engage in these inquiry activities, they will be able to share data freely, seamlessly trading the states of models or simulations to share, repeat, and extend individual explorations with simulations or seamlessly gather data taken from probes and sensors into aggregate datasets that uncover underlying patterns quickly and easily.

Teachers will have access to real-time data about student progress at all times, providing a detailed, ever-evolving picture of student learning. With this, they can tailor teaching responses, surface student misconceptions as they occur, and introduce vital ideas at just the right time in the learning process. Deeply digital curricula will also take the concept of “educative curricula” to its natural end, making use of real-time data to provide teachers with suggestions for teaching sequences (pedagogical patterns) they might use at a particular point in instruction or by suggesting dynamic groupings of students based on where students are in the learning process on a particular day. As they need, teachers will also be able to customize deeply digital curricula, adding in new concepts, or material that helps clarify a concept, and share their customizations in a larger community. Ultimately, connecting data about student learning with a rich field of customized curricula will permit rich new data mining to determine, for example, effective curricular sequences or best practices in instructional delivery helping further teacher learning.

Students will experience curricula and learning in a completely new way. In many instances, curricula will adapt to their needs as never before, assessing what they have already learned and making decisions about how best to scaffold subsequent learning based on a mix of research into learning progressions, a student’s remaining learning needs, and individual student preferences. Individualized and timely feedback will encourage students as they progress and provide leveled scaffolding and supports, even within complex topics. Deeply digital curricula will also offer many more opportunities for student choice, providing flexible, coherent presentations of topics that enable students to construct their own paths toward a coherent set of learning goals.


Bringing all these facets, or even subsets of them, together in one place will indeed be a significant challenge. Many of these principles have yet to be developed in full even in proof-of-concept terms, and bringing together two or more of these ideas generally involves integrating the work of two or more different projects, each with their own people, platforms, and central ideas. For most steps toward this vision to come into play, we will first need each of these attributes to become established in its own right. Once this is able to happen and the principles are taken as important individually or in small subsets, then they will be able to be combined. This will most likely result in the enactment of deeply digital learning as described above only in a second or third generation of work after the present stage.

Some of the individual aspects of deeply digital curricula come with large, daunting challenges. Accessing in-depth data about student work is a potentially transformative concept that is already making headway in some systems and is further discussed under the topic of embedded formative assessments [INCLUDE CITATIONS OR LINK TO THE WIKI PAGE]. These data come with their own privacy and access concerns, however, presenting challenges that are generally very significant to overcome, especially when people with multiple roles need to access the data or data is carried across state lines. Even with these concerns mitigated, the presence of such a rich stream of data creates its own problems when these data must be presented to a teacher such that the teacher can digest and act upon them efficiently. Adaptive techniques have progressed significantly over the past decades, but remain still far from the larger promise of providing an interactive, scaffolded curriculum, especially where such a curriculum demands robust artificial intelligence and natural language interpretation capability.

After we have addressed how to present this in-depth view into where students are at any given time to teachers, we will still have the challenge of helping teachers learn to use this data to make best use of its potential. For example, teachers will have access to formative assessment data the likes of which education has not yet seen. Teachers will have to learn to make use of these data. True formative assessment requires the teacher to regularly monitor the data and to make changes in teaching practice based on the information so that teaching takes into account where students are and what they need next. While there has been much use of the term “formative assessment” in conjunction with the rise in emphasis on assessment and measurement in education, most applications of what are termed formative assessment end up in practice behaving as summative assessments instead.

Another major challenge will be providing curricula that can supply coherent paths for learning while still enabling student choice and flexible paths for learning. Arguably the greatest value of developed curricula lies in the careful thinking that goes into their curricular scope and sequence and the careful path they lay out for learning. How a curriculum can still supply such coherence and care when it is unknown which portions of a curriculum students might encounter or in which order they might access them remains an unknown task. In addition, creating materials that guide teachers dynamically as they receive data about a student’s understanding to help them support learning is a herculean task. If we can build a system that knows what students have learned and demonstrated an understanding of, and if the system knows what good pedagogical patterns look like, then the deeply digital learning system should be able to present teachers with suggestions about the potential best next paths to pursue at any given moment. Providing such as system will require a much deeper research base and more intelligent designs than currently exist. Additionally, there are also unforeseen complexities and challenges in creating systems able to tackle the interactions required to support dynamic feedback for teaching and learning.

Dissenting views / critiques

The existence of data about learning on the individual student level and the linking of these data with individual curricula and teachers has notable potential drawbacks. Teachers could be evaluated based on this proliferation of student data. If this is done properly and as one of a robust set of measures, it could be useful, but a distinct danger lies in the use of such data in a superficial manner. On the contrasting side, having curricula that are deeply digital and involve increased reaction to students’ needs may entice some to feel that the role of teachers should be diminished or that teachers could be removed entirely. Not recognizing the vital need of teachers in conjunction with technology is a major oversight, but it remains a significant threat as digital curricula become more comprehensive. Also, by developing curricula that are more automatically responsive to individual learners’ needs, the potential could arise for isolation of learners. With the possibility of tailoring learning so completely to an individual on the horizon, the potential to separate people and permit them to learn at their own pace at the cost of important communal or social aspects of learning. Because learning in a deeply digital curriculum will provide open-ended possibilities, the role of the teacher will naturally need to change significantly as well. This will be a threat to some who are more comfortable with sequential or constrained learning sequences, and will demand significant new skills on the part of teachers.