After briefly surveying the history of knowledge, computing, programming, and software engineering, computing education will be reframed as empowering makers in the cognitive era. The makers' movement is about the democratization of the tools of self-expression and production. From global cloud-based deployment of apps on smart phones to nano-manipulation of advanced materials in custom jewelry and clothing with open designs downloadable for 3D printers, software empowers makers to co-create value in smart service systems. Smart service systems are based on provider platforms that enable customer to interact and co-create value together. In addition, cognitive assistants for all business occupations and societal roles are beginning to appear democratizing access to knowledge and expertise in smart service systems.

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In this paper, we investigate whether or not most novice programming bugs arise because students have misconceptions about the semantics of particular language constructs. Three high frequency bugs are examined in detail — one that clearly arises from a construct-based misconception, one that does not, and one that is less cut and dry. Based on our empirical study of 101 bug types from three programming problems, we will argue that most bugs are not due to misconceptions about the semantics of language constructs.

In this paper we propose one explanation of why some novice programs are buggier than others. Central to our explanation is the notion of merged goals/plans in which multiple goals are achieved in a single integrated plan. Our arguments are based on our theory of the knowledge — plans and goals — used by a novice in creating a program, and an analysis of actual buggy novice programs.

In this paper, we present a descriptive theory of buggy novice programs and a bug categorization scheme that is based on this theory. Central to this theory is the cognitively plausible knowledge - goals and plans - that underlies programming. The bug categorization scheme makes explicit problem-dependent goal and plan knowledge at many different levels of detail. We provide several examples of how the scheme permits us to focus on bugs in a way that facilitates generating plausible accounts of why the bugs may have arisen. In particular, our approach has led us to one explanation of why some novice programs are buggier than others. A basic part of this explanation is the notion of merged goals and merged plans in which a single integrated plan is used to achieve multiple goals.

In order to achieve state-of-the-art performance in a speaker-dependent speech recognition task, it is necessary to collect a large number of acoustic data samples during the training process. Providing these samples to the system can be a long and tedious process for users. One way to attack this problem is to make use of extra information from a data bank representing a large population of speakers. In this paper we demonstrate that by using Bayesian techniques, prior knowledge derived from speaker-independent data can be combined with speaker-dependent training data to improve system performance.

Dynamic programming is used in speech recognition to search efficiently for word sequences whose templates best match acoustic data. The search is constrained by finite-state networks embodying grammatical rules. Typically, dynamic programming is implemented in two steps: the first calculates, for each state in a network and for each time, the best way of arriving at that state at that time; the second traces back from the final state at the final time to the initial state at the initial time to determine the best path through the network. This second step cannot be initiated before the determination (usually from the detection of silence) that the final state has been reached. Such a determination is difficult in the recognition of truly continuous speech; there are often no reliable anchor points. Further, it is often desirable to be able to recognize at least part of an utterance before a speaker has stopped talking. In this paper we introduce a technique for discovering the initial section of the optimal path through a network before the traversal of the network is complete. It can be used to report a system's interpretation of acoustic data from the not-too-distant past without relying on or making any decisions which may degrade recognition accuracy.

To evaluate the performance of a speech recognition system, large databases of labeled speech, including various speakers, noise conditions, and vocabularies, are necessary. This paper describes a method for automatically labeling speech data. In the past, speech has been labeled manually, typically by listening to and viewing waveforms through real-time, interactive computer I/O stations. This process is slow and tedious, and accounts for the shortage of large speech databases. The automatic labeling method reported here uses dynamic programming to align a script which is produced as the output of a recognition system, and a known script. The alignment gives a tentative labeling which can be refined by repeating the training, recognition, and alignment processes. The method was used to label a 50 speaker database of 140,000 digits.