In online learning, the ability to predict (in advance) whether a learner will be correct or incorrect in answering a question would be quite helpful to course instructors. For one, such methods would allow instructors to identify trouble areas for specific learners before they arise. In this work, we study student performance prediction for Massive Open Online Courses (MOOCs), where such analytics are particularly useful, given that learners typically answer only a fraction of the questions available for the course. By the same token, these analytics are also more challenging to obtain, given the sparsity of the question-response data. To combat this, the algorithms we develop leverage behavioral (clickstream) data collected about learners as they interact with course content to predict their performance. Tested against a large set of empirical data, we find that our schemes outperform standard algorithms (i.e., those without behavioral data) for all datasets and metrics tested. Moreover, the improvement is particularly pronounced when considering the first few course weeks, demonstrating the “early detection” capability of such clickstream data.
How can we relate the actions that a learner takes during a course (i.e., their behavior) to the amount of knowledge the learner has gained through the course (i.e., their performance)? This fundamental question, pertaining both to education and to learning in general, is the focus of this work. We propose two different frameworks for representing learner behavior – one as a series of events and their associated durations, and another as a series of positions visited while traversing the course content – and apply these frameworks to two of our Massive Open Online Course (MOOC) datasets. In doing so, we are able to extract recurring behavioral patterns (i.e., “motifs”) that are significantly associated with whether a learner has gained knowledge from content or not, and to substantially improve the quality of performance prediction, verifying the efficacy of our methods in relating behavior and performance.
Many entrepreneurs found innovative enterprises only to find themselves failing as leaders. An entrepreneur's goal is making his or her vision a reality—a basically selfish, though beneficial, act. A leader, on the other hand, focuses on others, forging individuals with differing wants, needs, and visions into a winning team. Startup Leadership outlines exactly how one can balance these two opposing natures and defines 5 basic skills required to become an Entrepreneurial Leader.
Entrepreneurs must understand the critical transition points in the growth of all new enterprises to know when value creation needs to shift from project oriented activities to repetitive processes. This lack of understanding has resulted in unnecessary entrepreneurial failures.
Online and distance learning programs, including those for higher education and corporate training, are notorious for their issues in maintaining learner engagement. Adaptive learning is a potential solution to this issue, because of its ability to differentiate learning automatically based on a user model that can capture diverse learning styles and backgrounds. This work presents the design and preliminary evaluation of the Mobile Integrated and Individualized Course (MIIC), an Adaptive Educational System (AES) which integrates video, text, assessments, and social learning into a native mobile app for delivery to end users. The inputs collected about users as they interact with each learning mode can be used to update the user model, which is in turn used to drive the adaptation engine. Through two initial trials, it was found, for example, that the mean level of engagement – when quantified as the number of pages viewed – was statistically higher among distance learning students using MIIC than among those using a one-size-fits-all (OSFA) presentation of the same material.
This article describes why entrepreneurs that understand their true motivations have much greater chances of achieving their personal objectives.
Pitching skills are widely considered fundamental to basic entrepreneurship, and this skill is taught, evaluated, and rewarded as part of almost any entrepreneurial education. The problem is that pitching is a waste of time and teaching this as a primary skill of entrepreneurship is misleading and sets the wrong priorities for aspiring entrepreneurs.
Understanding the distinction between the value creation characteristics of projects and processes is critical in determining the best way to achieve innovative results in any enterprise.
Social learning has been identified as a key aspect of Massive Open Online Courses (MOOCs), because it holds the promise of scalable peer-based learning and is often the dominant channel in which students and instructors interact. Two key challenges faced by MOOC providers today are the rapid drop-off in participation on these forums over time, and the overflow of information on these forums that makes it difficult for students to navigate through and find desired information. This work performs a large-scale statistical analysis of the decline in participation over time for all courses offered by a MOOC provider over summer 2013, identifying course properties that are correlated with this decline. Further, it develops a model for the process by which the discussions are generated, which is used to design algorithms for ranking newly created forum threads by their relevance.