The use of technology in education is transforming classrooms, creating virtual learning environments, guiding and coaching students through the admissions and financial aid process, etc. (Carmean & Mizzi, 2010). Technology has infiltrated the academy and is used to transform learning but is also just merely used to mimic traditional learning and teaching methods. Much attention is given to transforming the classroom and ‘nudging’ students to access and completion, but how are technologies assisting students with study habits and tactics? We seem to be focused on all facets of transforming education through technological advances, but are we giving study skills and systems the same short shrift in the technological age as it was given in prior non-tech generations of education?

“Study skills are a constellation of competencies that allow students to acquire, record, organize, synthesize, remember, and use information” (Hoover & Patton, 1995; Fisher & Frey, 2017). I am focusing on study skills and systems because of my own experience with education. As a student in a college preparatory high school and first-generation college student, I was often ‘on my own’ to learn how to study. I remember at times sitting down with my textbook, class notes, study guide and saying to myself, “So, what do I do now?” I saw the frustration of first-semester college students when I taught a one- or two-week study skills and reading seminar at various colleges and universities throughout the U.S. If we are committed to helping students persist and complete, we must help students take the guesswork out of how to study.

It is not hard to envisage a not-too-distant future where students will be saying to an AI device:

• "Alexa, what test preparation and assignments do I need to work on today?"
• "Siri, how is my progress on my term paper?"
• "Google, how should I prepare for my practicum?"

Currently, it appears there is a mishmash of productivity mobile applications ("app(s)") being used by students to fill the gaps, but there is not a holistic program that prods the student or offers formative and summative feedback on study habits and knowledge progression. The example below is from a YouTube channel that offers advice on "7 FREE & ESSENTIAL Productivity Apps You Should Try." What is evident is, although the apps are free to all, it takes multiple apps to handle various time management and study tactics without the advantage of holistic feedback.

                                              (Mariana's Study Corner, 2017, March 4)

Cope and Kalantzis (2017, p. 13) offer a conceptual framework that outlines seven learning affordances to provide theoretical underpinnings for a reasoned application of technology in learning. Kalantzis and Cope (2018) offer the affordances under the conceptualization of reflexive pedagogy. Reflexive pedagogy is an evolution from didactic pedagogy and "is a more varied and open-ended process of knowledge making, moving backwards and forwards between different ways of making knowledge or knowledge processes" (Kalanzis & Cope, 2008, p. 273). A technological intervention for study skills such as a mobile app could incorporate these affordances to truly harness the full power and advantages of the technology.

Figure 1 (Kalantzis & Cope, 2017, p. 13)

Mastroianne (2017) structured questions to answer when developing an e-Affordance. Throughout this work the seven affordances will be addressed in light of a developing a app/program for study skills in accordance with the seven e-Affordances.

Thaker and Jayaram (2018) developed a beta app called StudyBuddyAI. They built the prototype using a Question Answering (QA) system which aims "to process some corpus of text and answer user defined questions with semantically correct responses" (2018). The architecture shown in the figure below demonstrates the flow of information and the three distinct phases in the learning loop of their design called the Intelligent Question Answering System. Combined with a model like e-Affordances a study skills app StudyBuddyAI could operate under its own reflexive pedagogy.

Figure 2 (Thaker & Jayaram, 2018)

The application beta is built on the AI models: Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and recurrent neural network (RNN). A video description of these models follows:

                                                                       (Nguyen, 2018)

Ubiquitous learning allows users to connect with content from computers, mobile devices, online/offline, and from anywhere and anytime (Cope & Kalantzis, 2017). This allows for an unprecedented opportunity to provide interventions for students in real-time. Study skills intervention technology could provide the opportunity for learners to:

• Access assistance with studying anywhere and anytime from any device
• Work off-line and still receive assistance
• The resources could change based on the need of the learner
• Draw on a variety of expertise and techniques

           (Adapted from Mastroianne, 2017)

An example of a current stand-alone app that offers this is the notetaking software Evernote. Schepman et al (2012) studied student use of Evernote and found undergraduate students from a range of disciplines readily adopted Evernote into their study strategies and the students identified three main uses: 1) organization, 2) information acquisition and 3) information management. The study found students who used the software in a mobile format (mobile app) were more likely to add notes in a variety of locations as opposed to non-mobile format users (web/PC), see figure below. Schepman et al (2012) concluded "the ubiquity of mobile note-taking software could make it attractive for students on professional placements, as they may be able to take notes and record thoughts and observations (p. 316)."

The question becomes do the benefits of device-based notetaking via accessibility to notes and the tendency to take more notes in a variety of locations outweigh the cognitive benefits of handwritten notes. Can other e-Affordances make up the gap?

Figure 3 (Schepman, 2012, p. 313)

Figure 4 (Schepman, 2012, p. 313)

In contrast, studies have shown (Mueller & Oppenheimer, 201; Bui et al, 2016; James & Englehardt, 2012) found the benefits of handwritten notetaking to include 1) greater understanding and retention than device-based notetaking; 2) after twenty-fours handwritten notetakers have better recall than device-based notetakers; and 3) handwritten notetaking engages more neural pathways than device-based pathways.

A study skills app could help the learner make new connections between pieces of information in order to create new meanings as part of the learning process. The intervention could help the learner build upon existing knowledge, assess and track what the learner already knows, in order to help the learner where they are in both process and product orientation (Cope & Kalantzis, 2017).

• The content can be learned step-by-step in a structured sequence or holistically
• The technology can spur the learner to consider potential applications and to develop new ideas or synthesis
• The technology can help the learner to make connections between key points
• The technology can adapt to the learners level of mastery

       (Adapted from Mastroianne, 2017)

Schepman et al (2012) found students taking notes on the Evernote platform used it to adopt innovative multimedia functions. Again, where the study found utility in device-based note-taking is in fieldwork. Some examples include, but are not limited to "create notes and/or photographic records (possibly with location tagging), documenting their experiences or findings" (p. 316). This type of active knowledge making can occur in real-time and does not require multiple modes to amass and synthesize disparate knowledge as occurs with analog tools (i.e. notepad, camera, map, etc.).

An application like Evernote does not currently have the AI capability of pointing out connections to the user, but merely allows the user the freedom to move and reconfigure different media. In contrast, an application like DuoLingo adapts to the users previous feedback based on answers to previous questions; however, it does not take into account user notes or other data collected or synthesized in regards to the subject matter (Kerr, 2015). Kerr (2015) summarized the types of adaptive learning and how it can present in the below table (p. 88).

Figure 5 (Kerr, 2015)

The problem remains of consolidating disparate data collected - from the learner and from the adaptive technology so it is synthesized and included in an app's advising and nudging features.

A study skills app can use or combine text, media, sound, and data resources to reach the learner in different ways based on the learner's unique learning style. Multimodal resources can better capture learner interest and adapt to the learner’s unique style of learning. Content presented in a myriad of ways it ensures that the learner can choose a preferred medium, but also have concepts reinforced along the way using the various mediums (Cope & Kalantzis, 2017).

• The technology can offer multimodal resources
• Learners have an opportunity to interact with material multi-modally
• Opportunities for both print and visual learning
• The resources can comply with universal learning principles

       (Adapted from Mastroianne, 2017)

How does one spur multimodal learning in a world of a flat screen? One can argue that apps like Duolingo, Evernote, etc. (Schepman, 2012; Kerr, 2015) offer multimodality by using audio, visual and textual elements. Handwritten notes - on a tablet offer a tactile experience. This technology can bridge the gap between the benefits of tactile handwritten notes ((Mueller & Oppenheimer, 2014; Bui et al, 2016; James & Englehardt, 2012) and digital notes that can be provide additional multi-modal benefits (Schepman, 2012). The video below details some of the newest digital handwriting technology that translates writing into digital text:

                                                  (Associated Press, 2019, January)

Recursive feedback is possibly the most important aspect of a study skills intervention as a way for learners to check their progress (Cope & Kalantzis, 2017). Timely and relevant feedback can be instantaneous and correct the students study approach in real-time. This helps to lead to metacognition and mindfulness of how the student is studying. The student does not have to sit down to study and ask themselves, “So, what do I do now?” 

• The learner can receive immediate feedback
• The feedback would relevant and related to the discipline and specific task
• The application can track the users progress and visualize it for the learner

       (Adapted from Mastroianne, 2017)

Villarreal and Martinez (2018) reviewed and identified three measures for assessing study skills in college students that could be adapted to a study skills app to provide feedback to learners. In Table 1 that follows, Villarreal and Martinez (2018) summarize the measures that assess student study skills that could best be applied to college students.

(Villarreal & Martinez, 2018, p. 633)

Villarreal and Martinez (2018), in Table 2 that follows, outlines constructs of the assessments for study skills for the various instruments.

(Villarreal & Martinez, 2018, p. 633)

Instead of these assessments being paper forms that are assessed by college personnel, the app program could assess the student using one or an adapted combination of the assessments and monitor the student's progress and show the student's progression. The student should have the agency to decide whom to share the assessments with. This could provide the students with feedback regarding their study skills and progress. For instance, Kerr (2015) discusses digital flascards in language learning and how an algorithm can be programed to adapt to the students' successfull an unsuccessful responses. Could the algorithm not also be simulataneously assessing how well students respond to flash cards or flash cards with images, or flash cards with audible text and learn the students' prefered styles as proposed by Thaker and Jayaram's (2018) architecural model of the AIStudyBuddy?

Any study skills app could facilitate peer learning and review and interaction with instructors. It could go beyond just the student’s classmates and allow for interactive knowledge and learning via interactions similar to what occurs in networked video games (Cope & Kalantzis, 2017).

• A technology app or program for study skills can lend itself to a forum or an online project space
• The app/program could prompt learners to answer focus questions and/or activity prompt
• The collaborative activity could asynchronous or synchronous
• There is the potential of shared goals of learning a particular concept or technique

       (Adapted from Mastroianne, 2017)

This does not mean that all human interaction would be replaced. Ying and Shulruf (2019) studied medical interns developing skill acquisition (suturing/ligature) using an experimental design. The study used technology-mediated and non-technological interventions in addition to the control group. Students were divided into expert-led tutoring, or expert-led+AI tutoring groups freely. The end-of-surgical block objective structured clinical examination (OSCE) performance and self-assessed confidence in suturing/ligature skills were highest in the expert-led+AI group. In the expert-led+AI group, the best performance and highest post-OSCE confidence were noted in those who engaged in three AI practice sessions.

Research suggests a negative relationship between frequent use of communication technologies, such as text messaging and social network sites, and academic performance, but the nature of the relationship needs to be explored in greater detail. Thompson (2017) had a total of 74 first-year university students completed the online Learning and Study Strategies Inventory and reported on how frequently they used text messaging, instant messaging, and online social networks such as Facebook. Correlation analysis indicated a negative relationship between frequency of communications technology use and the Learning and Study Strategies Inventory measure of concentration. This study and further research can help to better tailor study skills interventions in light of social media use.

Thinking about thinking is a valuable activity for learners who are assessing what are their best strategies and techniques to study. It helps the reflect on what they have learned and where they are going in their educational journey. It is a form of self reflection on how to learn. A technological intervention can help the learner determine areas of weakness as well as strengths and helps the learner to think about how they can be the best learner (Cope & Kalantzis, 2017).

• The app can spur metacognition activities by providing reflexive feedback
• The app can allow the learner assess the learner, and develop surveys, or quizzes for the learner
• The leaner can check their progress and see how they are assessed
• The collaborative facet allows the student to discuss their thoughts with others and self-reflect on study strategy with peers

         (Adapted from Mastroianne, 2017)

The paradigm shift that has to occur in terms of app for studying is that we must move beyond the current model of mere productivity apps which is what the current study apps tend to be (Lindberg, 2017). This is the key reason applying the affordances is so important in creating a study skills app. Chen et al (2017) induced a treatment group to: 1) think about how they were studying, and 2) think about how they could study more effectively going forward. The treatment group significantly outperformed the control group. Referencing back to Thaker & Jayaram's (2018) AIStudyBuddy app architecture model introduced in the theoretical and conceptual frame section of this review, it would seem simple to introduce a set of metacognitive questions for the AI to ask the student to inform and drive the adaptation of strategies and advice provided by a study skills app tailored to the student based on the student's own metacognitive insights.

Differentiated learning can be a central tenant of a study skills app/program and the technology is making it possible. The intervention can personalize the learning experience and tailor each study technique to the learner’s specific needs and personal learning style (Kerr, 2015; Cope & Kalantzis, 2017).

• The app/program can is learner-centered and adapts as the learner progresses
• There is real-time assessment and feedback of individual learning needs
• There relevant advice and support in real-time
• The pace can be adapted to the learner and the learner’s current capabilities

       (Adapted from Mastroianne, 2017)

All aspects discussed in this review leading up to this affordance has led to potentialities for differentiated learning geared toward the specific learner. The difficulty lies in the fact that there is a multitude of potential interventions via a wide of array of technologies. If students are lost about how to study, they may be overwhelmed by the sear volume of answers to help them be more productive and accomplish their work (Griffin et al, 2012; Oropoulos & Petronijevic, 2018). As evidenced by Chen et al (2017) the intervention does not need to be revolutionary, it must simply be utilized.

Carmean & Mizzi (2010) report that subtle interactions--or "nudges"--can influence people's actions without infringing on their freedom of choice. They argue that with the wealth of digital data in higher education nudge students to better achievement and persistence but also that the knowledge embedded in the machines makes it possible to do so without the guesswork of human design and intervention. As the authors see more independent, working, and older students in higher education, they propose that today's learners might just need a nudge. "Nudge analytics," or machine recommendations based on patterns found in the data, might be a better way of reaching these students: a personalized digital nudge to study, to come to class, to read the chapter assigned, to submit the assignment due tomorrow. The next step is to figure out correlation characteristics from machine recommendations, and determine practices based on patterns found in the data.

The literature leaves open a myriad of possibilities to study how a technological intervention can improve students' study skills and strategies. The focus of developers (i.e., Thaker & Jayaram 2018) appear focused on the mechanics of the intervention and would do well to scaffold using a conceptual framework like e-Affordances (Mastroianne, 2017; Mastroianne, 2017) to ensure the intervention addresses the needs of the learner and does not merely exist as an end in of itself.

Student interventions for study skills, such as a mobile application, must evolve from simple productivity tools to adaptive technologies such as an app or program that journeys with the learner through the education process. However, merely mimicking didactic era study skills such as notetaking and flashcards for the digital era is not enough. Using the seven (7) affordances as presented by Kalantzis and Cope offers great potential with current productivity apps and offers a promising scaffolding for the development of a holistic study skills technological intervention like an app.